DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Responsive to the communication dated 08/07/2023
Claims 1-38 are presented for examination
Information Disclosure Statement
The IDS dated 01/22/2024 has been reviewed. See attached.
Drawings
The drawings dated 08/07/2023 have been reviewed. They are accepted.
Abstract
The abstract dated 08/07/2023 has been reviewed. It has 144 words, and contains no legal phraseology. It is accepted.
Claim Objections
The claims have numerous issues with antecedent basis and structure. The Examiner suggests amending the claims such that the first recitation of each distinct element uses articles such as “a”/”an”, later recitations referring back to the same distinct element use articles such as “the”/”said”, to use disambiguating modifiers (e.g., first, second, etc.) when there are multiple distinct elements with the same base term, and that the use of modifiers for each distinct element are kept consistent. Note that particularly severe issues are rejected in the 112 section later in this action. Below is a non-exhaustive list of examples of these issues:
Claim 1 recites “defining the relative importance of defect types…” As no relative importance was previously introduced, this should instead read “defining a relative importance of defect types” to avoid potential issues with antecedent basis.
Claim 1 recites “an overall ranking of the defect set…” As no defect set was previously introduced, this should instead read “an overall ranking of a defect set…” to avoid potential issues with antecedent basis.
Claim 1 recites “determine optimal laminate construction…” To improve readability, this should instead read “determine an optimal laminate construction…”
Claim 1 recites “…that reduces the buildup of defects through the thickness of the laminate.” To avoid potential issues with antecedent basis, and as it is clear this laminate refers to the previously introduced subject laminate this should instead read “…that reduces a thickness of the subject laminate.”
Claim 2 recites “…for optimizing the selection of ply scenario input parameters…” To avoid potential issues with antecedent basis, this should instead read “…for optimizing a selection of ply scenario input parameters…”
Claim 3 recites “…wherein said input parameters…” To avoid potential issues with antecedent basis and improve readability, this should instead read “…wherein said ply scenario input parameters…”
Claim 4 recites “…Construct surrogate model based on training data…” To improve readability, this should instead read “…Construct a surrogate model…”
Claim 6 recites “use of one of (genetic algorithm (GA),” This first parenthesis appears to be a typo and should be removed to improve readability.
Claim 9 recites “the chosen layup strategy…” To avoid potential issues with antecedent basis as no layup strategy was previously chosen, this should instead read “the associated layup strategy” as it is clear that the associated layup strategy is what is being referred to.
Claim 9 recites “the optimal laminate construction…” As no optimal laminate construction was previously introduced, this should instead read “an optimal laminate construction…” to avoid potential issues with antecedent basis.
Claim 9 recites “…and the compounding effect they have throughout a laminate.” As it is clear the subject laminate is what is being referred to and no compounding effect was previously introduced, this should instead read “… and the subject laminate” to improve readability and avoid issues with antecedent basis.
Claim 10 recites “an iterative optimization of the possible respective ply geometries” To improve readability this should instead read “an iterative optimization of the respective possible ply geometries”
Claim 12 recites “wherein the placement strategy and seed point corresponds with…” To improve readability, this should instead read “wherein the placement strategy and seed point correspond with…”
Claim 13 recites “the frequency and severity of where defects are stacking…” as no frequency was previously introduced, this should instead read “a frequency and severity of where defects are stacking…” to avoid potential issues with antecedent basis and improve readability.
Claim 14 recites “by optimizing the selection of input parameters…” As no selection was previously introduced, this should instead read “by optimizing selection of input parameters…” to avoid potential issues with antecedent basis and improve readability.
Claim 16 recites “…defect stacking for resulting uniform through-thickness laminate…” To improve readability this should instead read “…defect stacking for a resulting uniform through-thickness laminate…”
Claim 21 recites “for further optimizing the interaction of defects…” To improve readability and avoid potential issues with antecedent basis, this should instead read “for further optimizing
Claim 24 recites “during the ply optimization process…” It is clear that what is being referred to here is the ply level design space search introduced in claim 16; therefore, to avoid potential issues with antecedent basis, this should instead read “during the ply level design space search…”
Claim 25 recites “a plurality of respective of said laminate scenarios…” As the previously introduced “laminate scenario” was singular, this should be amended to read “a plurality of
Claim 31 recites “updating the levels of defect interaction…” As no levels of defect interaction were previously introduced, this should be amended to instead read “updating
Claim 34 recites “for creating a severity to compare…” As it is clear this was meant to read “for creating a severity score to compare…” it is recommended to amend the claim as such to improve readability, as well as amended the equation in a similar manner.
Claim 34 uses the variable “Level” in the equation; while the variable ALevel was introduced previously and corresponds to the area of a particular level and nlevels refers to the total number of levels, it is unclear what aspect of a particular level is referred to with the variable “Level” Amendments to further clarify this are recommended.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-9, 12, 19, 23-25, 27-29, and 36-38 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “large structures” in claim 1 is a relative term which renders the claim indefinite. The term “large structures” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. What size is sufficient for a structure to be considered “large” is a matter of subjective opinion that may be different from person to person.
The term “complex” in claim 1 is a relative term which renders the claim indefinite. The term “complex” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. What level of complexity is required for a structure to be considered “complex” and where exactly the line that separates a “complex” structure from a “simple” structure is placed is a matter of subjective opinion that may be different from person to person.
Claim 4 recites the limitation "Search surrogate model for next location of interest..." As no location of interest was previously introduced, there is insufficient antecedent basis for this limitation in the claim.
Claim 4 recites the limitation "Run analysis and update model at the new locations found by search...” It is unclear what new locations are referred to here, as the only previous introduced location, the “next location of interest” is a singular location and not plural, as these new locations are. As such, there is insufficient antecedent basis for this limitation in the claim.
Claims 7, 19, and 27 contain the trademark/trade name “Vericut” and clam 28 further uses the acronym “VCP” to refer to “Vericut”. Where a trademark or trade name is used in a claim as a limitation to identify or describe a particular material or product, the claim does not comply with the requirements of 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph. See Ex parte Simpson, 218 USPQ 1020 (Bd. App. 1982). The claim scope is uncertain since the trademark or trade name cannot be used properly to identify any particular material or product. A trademark or trade name is used to identify a source of goods, and not the goods themselves. Thus, a trademark or trade name does not identify or describe the goods associated with the trademark or trade name. In the present case, the trademark/trade name is used to identify/describe a particular commercial software and, accordingly, the identification/description is indefinite.
Claim 12 recites the limitation “…corresponds with the starting point and the chosen layup strategy for each respective ply geometry.” There is insufficient antecedent basis for this limitation in the claim, as no starting point or chosen layup strategy was introduced previously. Further, as it is clear that the possible ply geometries are what is being referred to, “each respective ply geometry” should be changed to “each respective possible ply geometry”.
Claim 23 recites the limitation the subsequent predicted defect geometry…” As no predicted defect geometry was previously introduced, there is insufficient antecedent basis for this limitation in the claim.
Claim 36 recites the limitation “the score of each scenario…” As no scenarios nor any associated scores were previously introduced, there is insufficient antecedent basis for this limitation in the claim.
Claim 38 recites “all scenarios in the associated ply…” As no scenarios in an associated ply were previously introduced, there is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-38 are rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Claim 1 (Statutory Category – Process)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claim recites a mental process, specifically:
MPEP 2106.04(a)(2)(Ill): “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.”
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
Methodology for process planning for Automated Fiber Placement (AFP) manufacturing for identifying optimal starting point location and layup strategy for each ply of a subject laminate, for producing complex structures and large structures, comprising: providing one or more processors programmed for conducting Computer-Aided Process Planning (CAPP), wherein said one or more processors are programmed for iteratively determining: (1) a plurality of ply scenarios by locating respective starting points and associated layup strategies and subsequently presenting resulting geometrical fiber defect instances and severity measurements,
Determining a plurality of ply scenarios by defining starting points and layup strategies is a mental process equivalent to coming up with a series of such starting points, deciding how the layup process should be done for each, and creating a depiction of each ply according to the respective starting points and layup strategy.
Judging what kind of fiber defects would be produced for a certain ply is a mental process equivalent to observing the depiction of the ply and recognizing various defects. For example, if the depiction of the ply made according to the respective starting points and layup strategy shows frequent, significant spaces left between tows, it can be easily judged that severe gap defects have been created. These defect instances and their severities can be presented by merely indicating them on the depiction.
Performing this on a computer amounts to no more than mere instructions to apply.
Should it be found that presenting the resulting defect instances and severity measurements is not a mental process, it is also an example of insignificant post-solution activity.
(2) defining the relative importance of defect types to create an overall ranking of the defect set that is used for ply level optimization, and
Defining such relative importances is a mental process equivalent to judging what these importances should be, for example a person might decide that gaps should be 50% more important than overlaps.
(3) determining final scores for each ply scenario, so that ply level defects are combined to determine optimal laminate construction that reduces the buildup of defects through the thickness of the laminate.
Determining these scores is a mental process equivalent to observing each created ply depiction and judging their scores based on the previously determined importance rankings and the defects present in that ply. For example in a case where gaps were determined to be the most important defect, a ply with 100 severe gap instances would be scored lower than a ply with only a single, mild gap. Determining an optimal laminate construction based on this process is merely the act of coming up with a series of plies that have the best final scores.
Step 2A – Prong 2: Integrated into a Practical Solution?
Insignificant Extra-Solution Activity (MPEP 2106.05(g)) has found mere data gathering and
post solution activity to be insignificant extra-solution activity.
Post-solution activity:
presenting resulting geometrical fiber defect instances and severity measurements,
Presenting these defect instances and severity measurements that were determined through the use of a mental process amounts to no more than the presentation of the results of that mental process, and therefore is merely insignificant post-solution activity.
This element merely presents on the results of the previous abstract steps. A claim element that merely acts on a series of previous abstract steps is not indicative of integration into a practical solution nor evidence that the claim provides an inventive concept, as exemplified by ((MPEP 2106.05)(g)(Insignificant application) i. Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) and ii. Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55.)
Mere Instructions to Apply (MPEP 2106.05(f)) has found that merely applying a judicial exception such as an abstract idea, as by performing it on a computer, does not integrate the claim into a practical solution.
Mere Instructions to Apply:
Mere Instructions To Apply An Exception (MPEP 2106.05(f)) has found that simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. In light of this, the additional generic computer component elements of “providing one or more processors programmed for conducting Computer-Aided Process Planning (CAPP), wherein said one or more processors are programmed…” are not sufficient to integrate a judicial exception into a practical application nor provide evidence of an inventive concept.
Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitations are Insignificant Extra-Solution Activity or Mere Instructions to Apply and do not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.
Insignificant Extra-Solution Activity (MPEP 2106.05(g)) has found mere data gathering and
post solution activity to be insignificant extra-solution activity.
Post-solution activity:
presenting resulting geometrical fiber defect instances and severity measurements,
Presenting these defect instances and severity measurements that were determined through the use of a mental process amounts to no more than the presentation of the results of that mental process, and therefore is merely insignificant post-solution activity.
This element merely presents on the results of the previous abstract steps. A claim element that merely acts on a series of previous abstract steps is not indicative of integration into a practical solution nor evidence that the claim provides an inventive concept, as exemplified by ((MPEP 2106.05)(g)(Insignificant application) i. Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) and ii. Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55.)
Mere Instructions To Apply An Exception (MPEP 2106.05(f)) has found that simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. In light of this, the additional generic computer component elements of “providing one or more processors programmed for conducting Computer-Aided Process Planning (CAPP), wherein said one or more processors are programmed…” are not sufficient to integrate a judicial exception into a practical application nor provide evidence of an inventive concept.
The additional elements have been considered both individually and as an ordered combination in the consideration of whether they constitute significantly more, and have been determined not to constitute such.
The claim is ineligible.
Claim 2 recites “wherein said one or more processors are programmed for optimizing the selection of ply scenario input parameters using surrogate-based methods.”
Optimizing input parameters for a certain system is a mental process of deciding what parameters should be input, evaluating what the output of the system would be given those inputs, judging that output in relation to other outputs, and repeating this process until the best combination of inputs that provide the best output are found. For example, if that system is represented by an equation with two input variables within a certain range and an output, different inputs can be tested, mentally calculating the result, until the set that generates the best output is determined. Doing such optimization using surrogate-based methods on a computer amounts to no more than mere instructions to apply; see below.
Applying a computer to perform generic surrogate-based optimization at a high level of generality is simply the act of instructing a computer to perform generic functions to perform that optimization, which is merely an instruction to apply a computer to the judicial exception. The claim only recites the idea of a solution or outcome, i.e. that the selection of input parameters is “optimized” using “surrogated-based methods” without reciting how this optimization is actually accomplished. Further, the computer elements claimed are cited as merely generic tools to perform the operations; for additional clarity see ([Par 131-132] “Therefore, the surrogate model is providing an area of optimal performance not the exact location, but experimentation showed that both typically coincide. The selection of a regression model results in various types of SBO. One of the most popular options is Gaussian Process Regression (GPR) which results in Bayesian Optimization (BO) techniques. However, other regression models are also available and can be utilized in different ways. The regression models chosen to be evaluated here are Gaussian Process (GP), Support Vector (SV), and Random Forest” [Par 32] “Other example aspects of the present disclosure are directed to systems, apparatus, tangible, non-transitory computer-readable media, user interfaces, memory devices, and electronic smart devices or the like. To implement methodology and technology herewith, one or more processors may be provided, programmed to perform the steps and functions as called for by the presently disclosed subject matter, as will be understood by those of ordinary skill in the art.”)
The courts have found that such mere instructions to apply are not indicative of integration into a practical application nor recitation of significantly more than the judicial exception (MPEP 2106.05(f) “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983”)
Claim 3 recites “wherein said input parameters include at least one of starting points, layup strategies, and tows per course.”
This merely clarifies the form of the input parameters and is therefore merely an extension of the mental process and mere instructions to apply.
Claim 4 recites “wherein the surrogate-based methods comprise surrogate-based optimization (SBO) following the steps of:(1) Initial sample selection, (2) Construct surrogate model based on training data, (3) Search surrogate model for next location of interest, (4) Run analysis and update model at the new locations found by search, and (5) Repeat steps (2) through (4) until a convergence criterion is met.”
Selecting a set of samples is a mental process that merely consists of judging which samples from a larger set should be further considered.
Determining locations of interest is a mental process equivalent to judging, given a set of locations, which should be investigated further. Performing this process on a computer using a surrogate model amounts to no more than mere instructions to apply; see below.
Applying a computer to construct a surrogate model, determine locations of interest, and repeatedly running analyses and updating that model at a high level of generality is simply the act of instructing a computer to perform generic functions to perform that model construction, searching, analysis, and updating which is merely an instruction to apply a computer to the judicial exception. The claim only recites the idea of a solution or outcome, i.e. that surrogate model is “constructed” and various searching, analysis, and updated steps are run using the model without reciting how this construction nor searching, analysis, and updating steps are actually accomplished. Further, the computer elements claimed are cited as merely generic tools to perform the operations; for additional clarity see ([Par 131-132] “Therefore, the surrogate model is providing an area of optimal performance not the exact location, but experimentation showed that both typically coincide. The selection of a regression model results in various types of SBO. One of the most popular options is Gaussian Process Regression (GPR) which results in Bayesian Optimization (BO) techniques. However, other regression models are also available and can be utilized in different ways. The regression models chosen to be evaluated here are Gaussian Process (GP), Support Vector (SV), and Random Forest” [Par 32] “Other example aspects of the present disclosure are directed to systems, apparatus, tangible, non-transitory computer-readable media, user interfaces, memory devices, and electronic smart devices or the like. To implement methodology and technology herewith, one or more processors may be provided, programmed to perform the steps and functions as called for by the presently disclosed subject matter, as will be understood by those of ordinary skill in the art.”)
The courts have found that such mere instructions to apply are not indicative of integration into a practical application nor recitation of significantly more than the judicial exception (MPEP 2106.05(f) “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983”)
Claim 5 recites “wherein the surrogate-based methods include use of a regression model including one of Gaussian Process (GP), Support Vector (SV), and Random Forest (RF).”
This merely clarifies the form of the surrogate-based method and is therefore merely an extension of the mental process and mere instructions to apply.
Claim 6 recites “wherein the surrogate-based methods include use of a gradient free method including use of one of (genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA).”
This merely clarifies the form of the surrogate-based method and is therefore merely an extension of the mental process and mere instructions to apply.
Claim 7 recites “wherein the resulting geometrical fiber defect instances and severity measurements are presented using a communication with VERICUT Composite Programming.”
Communicating with software in a generic manner at a high level of generality, without any specificity as to how this communication is facilitated, amounts to no more than mere instructions to apply.
Claim 8 recites “wherein the geometrical fiber defect instances comprise fiber gaps, overlaps, angle deviation, and degree of steering.”
This merely clarifies the form of the defect instances and therefore is merely an extension of the mental process, mere instructions to apply, and insignificant post-solution activity.
Claim 9 recites “wherein the final scores are ranked for each ply scenario organized by starting point and the chosen layup strategy, chosen so that ply level defects are combined to determine the optimal laminate construction that reduces the buildup of defects through the thickness of the laminate, so that process planners create fiber paths that mitigate the number of geometry-based defects and the compounding effect they have throughout a laminate.”
Ranking the ply scenarios by final score is a mental process equivalent to observing the score associated with each ply scenario and ordering that by that score. Combining these plies to determine an optimal laminate construction is merely the act of collecting the best scoring ply scenarios into a single structure.
Claim 10 (Statutory Category – Process)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claim recites a mental process, specifically:
MPEP 2106.04(a)(2)(Ill): “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.”
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
A down selection process of fiber placement strategies and seed points for Automated Fiber Placement (AFP) manufacturing in order to independently generate optimized plies to be used together for a composite structure to be AFP manufactured, the process comprising: evaluating and predicting geometrically defined fiber defects of a plurality of possible ply geometries for use in the composite structure; and performing an iterative optimization of the possible respective ply geometries as a function of the placement strategy and seed point, where the optimization minimizes impact of resulting fiber defects in the composite structure.
Determining a plurality of possible ply geometries by defining seed points and placement strategies is a mental process equivalent to coming up with a series of such seed points, deciding how the layup process should be done for each based on the placement strategy, and creating a depiction of each ply according to the respective starting points and layup strategy.
Judging what kind of fiber defects would be produced for a certain ply is a mental process equivalent to observing the depiction of the ply and recognizing various defects. For example, if the depiction of the ply made according to the respective starting points and layup strategy shows frequent, significant spaces left between tows, it can be easily judged that severe gap defects have been created.
Optimizing the structure to minimize fiber defects is merely the act of using the defect judgements to determine the least defective plies and collecting them into a laminate structure. For each new ply in the structure, the process can be repeated with new seed points and placement strategies until a better, less defective ply geometry is found or it is determined that no better geometries exist.
Performing this on a computer amounts to no more than mere instructions to apply.
Step 2A – Prong 2: Integrated into a Practical Solution?
No additional elements are recited; therefore no additional elements integrate the abstract idea into a practical application nor provide an inventive concept.
Step 2B: Claim provides an Inventive Concept?
No additional elements are recited; therefore no additional elements integrate the abstract idea into a practical application nor provide an inventive concept.
The claim is ineligible.
Claim 11 recites “wherein the geometrically defined fiber defects include at least one of fiber gaps, overlaps, angle deviation, and degree of steering.”
This merely clarifies the form of the defect instances and therefore is merely an extension of the mental process.
Claim 12 recites “wherein the placement strategy and seed point corresponds with the starting point and the chosen layup strategy for each respective ply geometry.
This merely defines the relationship that the starting points and layup strategies have with the seed point and placement strategy, and therefore is merely an extension of the mental process.
Claim 13 recites “wherein the optimization minimizes the frequency and severity of where defects are stacking on top of each other in the composite structure.”
This merely clarifies the purpose of the optimization and is therefore merely an extension of the mental process.
Claim 14 recites “wherein defects are minimized by optimizing the selection of input parameters using surrogate-based methods.”
Optimizing input parameters for a certain system is a mental process of deciding what parameters should be input, evaluating what the output of the system would be given those inputs, judging that output in relation to other outputs, and repeating this process until the best combination of inputs that provide the best output are found. For example, if that system is represented by an equation with two input variables within a certain range and an output, different inputs can be tested, mentally calculating the result, until the set that generates the best output is determined. Doing such optimization using surrogate-based methods on a computer amounts to no more than mere instructions to apply; see below.
Applying a computer to perform generic surrogate-based optimization at a high level of generality is simply the act of instructing a computer to perform generic functions to perform that optimization, which is merely an instruction to apply a computer to the judicial exception. The claim only recites the idea of a solution or outcome, i.e. that the selection of input parameters is “optimized” using “surrogated-based methods” without reciting how this optimization is actually accomplished. Further, the computer elements claimed are cited as merely generic tools to perform the operations; for additional clarity see ([Par 131-132] “Therefore, the surrogate model is providing an area of optimal performance not the exact location, but experimentation showed that both typically coincide. The selection of a regression model results in various types of SBO. One of the most popular options is Gaussian Process Regression (GPR) which results in Bayesian Optimization (BO) techniques. However, other regression models are also available and can be utilized in different ways. The regression models chosen to be evaluated here are Gaussian Process (GP), Support Vector (SV), and Random Forest” [Par 32] “Other example aspects of the present disclosure are directed to systems, apparatus, tangible, non-transitory computer-readable media, user interfaces, memory devices, and electronic smart devices or the like. To implement methodology and technology herewith, one or more processors may be provided, programmed to perform the steps and functions as called for by the presently disclosed subject matter, as will be understood by those of ordinary skill in the art.”)
The courts have found that such mere instructions to apply are not indicative of integration into a practical application nor recitation of significantly more than the judicial exception (MPEP 2106.05(f) “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983”)
Claim 15 recites “wherein defects are minimized prior to manufacture of the composite structure by optimizing the selection of at least one of starting points, layup strategies, and tows per course.”
This merely clarifies when optimization is performed and what selections are controlled by the previously introduced selection process, and is therefore merely an extension of the mental process and mere instructions to apply.
Claim 16 (Statutory Category – Process)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claim recites a mental process, specifically:
MPEP 2106.04(a)(2)(Ill): “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.”
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
A process planning methodology for Automated Fiber Placement (AFP) for mitigating gap and overlap defect stacking for resulting uniform through-thickness laminate, said methodology comprising: providing one or more processors programmed for: using surrogate modeling optimization to search a ply level design space and capture local ply level optimums for a planned laminate; and
Searching a ply level design space and capturing ply level optimums is a mental process equivalent to creating various ply designs and depictions thereof, judging the how defective each design is by observing them and recognizing the number and severity of defects, and based on this determining which designs are the least effective.
Using computer-based surrogate modelling to perform this process amounts to no more than mere instructions to apply. See the analysis below.
conducting Computer-Aided Process Planning (CAPP), for iteratively determining stacked ply level optimums.
Performing process planning steps to determine these stacked optimums is a mental process equivalent to repeating the ply level design space exploration above for a series of additional ply designs to be stacked on the first ply.
Performing this planning using a computer amounts to no more than mere instructions to apply. See the analysis below.
Step 2A – Prong 2: Integrated into a Practical Solution?
Mere Instructions to Apply (MPEP 2106.05(f)) has found that merely applying a judicial exception such as an abstract idea, as by performing it on a computer, does not integrate the claim into a practical solution.
Mere Instructions to Apply:
providing one or more processors programmed for: using surrogate modeling optimization to search a ply level design space and capture local ply level optimums for a planned laminate; and conducting Computer-Aided Process Planning (CAPP), for iteratively determining stacked ply level optimums.
Applying a computer to perform generic modelling and optimization at a high level of generality is simply the act of instructing a computer to perform generic functions to perform that modelling and optimization, which is merely an instruction to apply a computer to the judicial exception. The claim only recites the idea of a solution or outcome, i.e. that the design space is “searched” and optimums are “captured” and “determining” without reciting how this modelling nor optimization is actually accomplished. Further, the computer elements claimed are cited as merely generic tools to perform the operations; for additional clarity see ([Par 131-132] “Therefore, the surrogate model is providing an area of optimal performance not the exact location, but experimentation showed that both typically coincide. The selection of a regression model results in various types of SBO. One of the most popular options is Gaussian Process Regression (GPR) which results in Bayesian Optimization (BO) techniques. However, other regression models are also available and can be utilized in different ways. The regression models chosen to be evaluated here are Gaussian Process (GP), Support Vector (SV), and Random Forest” [Par 32] “Other example aspects of the present disclosure are directed to systems, apparatus, tangible, non-transitory computer-readable media, user interfaces, memory devices, and electronic smart devices or the like. To implement methodology and technology herewith, one or more processors may be provided, programmed to perform the steps and functions as called for by the presently disclosed subject matter, as will be understood by those of ordinary skill in the art.”)
The courts have found that such mere instructions to apply are not indicative of integration into a practical application nor recitation of significantly more than the judicial exception (MPEP 2106.05(f) “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983”)
Moreover, Mere Instructions To Apply An Exception (MPEP 2106.05(f)) has found that simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. In light of this, the additional generic computer component elements of “Providing one or more processors programmed for: using surrogate modeling optimization … conducting Computer-Aided Process Planning (CAPP)” are not sufficient to integrate a judicial exception into a practical application nor provide evidence of an inventive concept.
Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitations are mere instructions to apply and do not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.
Mere Instructions to Apply (MPEP 2106.05(f)) has found that merely applying a judicial exception such as an abstract idea, as by performing it on a computer, does not integrate the claim into a practical solution.
Mere Instructions to Apply:
providing one or more processors programmed for: using surrogate modeling optimization to search a ply level design space and capture local ply level optimums for a planned laminate; and conducting Computer-Aided Process Planning (CAPP), for iteratively determining stacked ply level optimums.
Applying a computer to perform generic modelling and optimization at a high level of generality is simply the act of instructing a computer to perform generic functions to perform that modelling and optimization, which is merely an instruction to apply a computer to the judicial exception. The claim only recites the idea of a solution or outcome, i.e. that the design space is “searched” and optimums are “captured” and “determining” without reciting how this modelling nor optimization is actually accomplished. Further, the computer elements claimed are cited as merely generic tools to perform the operations; for additional clarity see ([Par 131-132] “Therefore, the surrogate model is providing an area of optimal performance not the exact location, but experimentation showed that both typically coincide. The selection of a regression model results in various types of SBO. One of the most popular options is Gaussian Process Regression (GPR) which results in Bayesian Optimization (BO) techniques. However, other regression models are also available and can be utilized in different ways. The regression models chosen to be evaluated here are Gaussian Process (GP), Support Vector (SV), and Random Forest” [Par 32] “Other example aspects of the present disclosure are directed to systems, apparatus, tangible, non-transitory computer-readable media, user interfaces, memory devices, and electronic smart devices or the like. To implement methodology and technology herewith, one or more processors may be provided, programmed to perform the steps and functions as called for by the presently disclosed subject matter, as will be understood by those of ordinary skill in the art.”)
The courts have found that such mere instructions to apply are not indicative of integration into a practical application nor recitation of significantly more than the judicial exception (MPEP 2106.05(f) “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983”)
Moreover, Mere Instructions To Apply An Exception (MPEP 2106.05(f)) has found that simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. In light of this, the additional generic computer component elements of “Providing one or more processors programmed for: using surrogate modeling optimization … conducting Computer-Aided Process Planning (CAPP)” are not sufficient to integrate a judicial exception into a practical application nor provide evidence of an inventive concept.
The additional elements have been considered both individually and as an ordered combination in the consideration of whether they constitute significantly more, and have been determined not to constitute such.
The claim is ineligible.
Claim 17 recites “wherein said one or more processors are further programmed for generating a reference curve for use in ply level design of the planned laminate.”
Generating a reference curve is a mental process equivalent to coming up with a curve starting at a particular starting point, as by drawing such a curve with a pen and paper.
Claim 18 recites “wherein the reference curve is generated through use of one of fixed angle, geodesic, and variable angle methodologies.”
This merely clarifies the particular way in which the drawn reference curve is created, and is therefore merely an extension of the mental process of drawing it.
Claim 19 recites “wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) in connection with VERICUT Composite Programming (VCP).”
Planning a process is a mental process equivalent to combing with a such a plan. Doing this on a computer and in connection with certain software in a generic manner amounts to no more than mere instructions to apply this mental process on a computer.
The courts have found that such mere instructions to apply are not indicative of integration into a practical application nor recitation of significantly more than the judicial exception (MPEP 2106.05(f) “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983”)
Claim 20 recites “wherein the planned laminate comprises a strut and the determined stacked ply level optimums comprise a strut geometry optimal for limiting defects with predicted manufacturability scores.”
Specifying that the laminate comprises a strut merely clarifies the structure of the laminate and therefore is merely an extension of the mental process and mere instructions to apply.
Further, predicting a manufacturability score is a mental process equivalent to observing a particular design and making a judgment about how difficult it would be to manufacture, assigning a score value to it. For example, a person might judge that a design for a traditional strut should have a higher manufacturability score than a design for a strut that spans the width of the solar system.
Claim 21 recites “wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for further optimizing the interaction of defects between individual plies through the thickness of the laminate.”
Optimizing the interaction of defects between individual plies is a mental process equivalent to collecting sets of plies that would not further exaggerate the buildup of defects. For example, if a first ply has an overlap at a certain position increasing its thickness in that location, placing another ply with a similarly located overlap on top of it might be avoid to prevent the overlaps from compounding into an even more uneven thickness.
Performing this process on a computer amounts to no more than mere instructions to apply.
Claim 22 recites “wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for beginning with a laminate skeleton which defines ply boundaries and associated fiber angles”
Creating such a laminate skeleton is a mental process equivalent to drawing a representation of it that indicates the ply boundaries and fiber angles, as with a pen and paper.
Performing this process on a computer amounts to no more than mere instructions to apply.
Claim 23 recites “wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for planning a laminate scenario which comprises a collection of ply scenarios which respectively carry over starting point location, path geometry, and the subsequent predicted defect geometry.”
Given the previously created various ply designs, creating such a laminate scenario is merely the act of organizing sets of ply designs together, and is therefore merely a mental process. A depiction of the laminate scenario could be created by drawing the various constituent plies stack on top of each other.
Performing this process on a computer amounts to no more than mere instructions to apply.
Claim 24 recites “wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for using individual ply scores calculated during the ply optimization process to create a ply summary score, describing the average quality of the collection of ply scenarios forming the laminate scenario.”
Calculating such a ply summary score by averaging previously generated scores is a mathematic calculation and therefore merely a mathematic concept.
Performing this process on a computer amounts to no more than mere instructions to apply.
Claim 25 recites “wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for evaluating a plurality of respective of said laminate scenarios as a function of defect interactions from the individual plies.”
Evaluating the laminate scenarios is a mental process equivalent to judging how various plies would interact when stacked. For example, if a first ply has an overlap at a certain position increasing its thickness in that location, a scenario that places another ply with a similarly located overlap on top of it might be judged to compound the issue into an overall more uneven thickness.
Performing this process on a computer amounts to no more than mere instructions to apply.
Claim 26 recites “wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for considering the defects for all plies globally and projecting them onto a common surface.”
Considering all plies globally merely specifics that all plies should be evaluated, and is therefore merely an extension of the mental process and mere instructions to apply. Projecting the results onto a common surface merely specifies how the defects are presented and is a mental process equivalent to drawing indications of such defects on a drawn depiction of a surface.
Performing this process on a computer amounts to no more than mere instructions to apply.
Claim 27 recites “wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for predicting tow gaps, overlaps, angle deviations, and steering performed with the VERICUT Composite Programming (VCP) tool.”
Judging what kind of fiber defects would be produced for a certain ply is a mental process equivalent to observing A depiction of the ply and recognizing various defects. For example, if the depiction of the ply made according to the respective starting points and layup strategy shows frequent, significant spaces left between tows, it can be easily judged that severe gap defects have been created.
Performing this process on a computer amounts to no more than mere instructions to apply; see the analysis below.
Generically predicting defects using modelling software, without specifying how that modelling is performed, amounts to no more than mere instructions to apply.
Applying a computer to perform generic modelling and prediction at a high level of generality is simply the act of instructing a computer to perform generic functions to perform that modelling and prediction, which is merely an instruction to apply a computer to the judicial exception. The claim only recites the idea of a solution or outcome; i.e. certain defects are “predicted” using software without reciting how this is actually accomplished. Further, the computer elements claimed are cited as merely generic tools to perform the operations; for additional clarity see ([Par 131-132] “Therefore, the surrogate model is providing an area of optimal performance not the exact location, but experimentation showed that both typically coincide. The selection of a regression model results in various types of SBO. One of the most popular options is Gaussian Process Regression (GPR) which results in Bayesian Optimization (BO) techniques. However, other regression models are also available and can be utilized in different ways. The regression models chosen to be evaluated here are Gaussian Process (GP), Support Vector (SV), and Random Forest” [Par 32] “Other example aspects of the present disclosure are directed to systems, apparatus, tangible, non-transitory computer-readable media, user interfaces, memory devices, and electronic smart devices or the like. To implement methodology and technology herewith, one or more processors may be provided, programmed to perform the steps and functions as called for by the presently disclosed subject matter, as will be understood by those of ordinary skill in the art.”)
The courts have found that such mere instructions to apply are not indicative of integration into a practical application nor recitation of significantly more than the judicial exception (MPEP 2106.05(f) “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983”)
Claim 28 recites “wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for exporting resulting course to course overlaps from VCP as STEP files defining each gap and overlap instance as a unique closed contour, where fiber angle deviation and steering are defined on a regular grid over a tool surface.”
Exporting data as a file amounts to no more than storing that data in memory in a certain format, which amounts to no more than mere instructions to apply.
The courts have explicitly recognized storing and retrieving data in memory as an example of mere instructions to apply; see MPEP 2106.05(f)(2) “(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). … TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of performing these functions using a telephone unit and a server and noted that these elements were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and operate as a digital camera including compressing images and transmitting those images, and the server simply receives data, extracts classification information from the received data, and stores the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at 1747-48. In other words, the claims invoked the telephone unit and server merely as tools to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on a telephone network without any recitation of details of how to carry out the abstract idea”
Claim 29 recites “wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for relying on a parametric domain of the tool surface models as the basis for defect discretization, with the tool surfaces represented through non-uniform rational b-splines (NURBS).”
This merely further clarifies how the data representing the tool surfaces is stored, and therefore is merely an extension of the mental process mere instructions to apply.
Claim 30 recites “wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for identifying stacked defects throughout the entire thickness of the laminate.”
Identifying stacked defects throughout the thickness of the laminate is a mental process equivalent to observing each ply of the laminate and judging visually where certain defects are located.
Performing this process on a computer amounts to no more than mere instructions to apply.
Claim 31 recites “wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for computing defect interactions beginning from the base ply and updating the levels of defect interaction as additional plies are considered.
Identifying stacked defects throughout the thickness of the laminate is a mental process equivalent to observing each ply of the laminate and judging visually where certain defects are located.
Performing this analysis in a certain order is merely an extension of this mental process.
Recording and updating measure of defect interaction is merely a mental process equivalent to, for example, writing down notes to keep track of the observed defects, as with a pen and paper.
Performing this process on a computer amounts to no more than mere instructions to apply.
Claim 32 recites “wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for measuring and scoring stacked defects relying on determined defect levels.”
Measuring and scoring stacked defects is a mental process equivalent to judging the severity and/or frequency of certain defects, assigning this judgment a certain score, and recording these scores, as with a pen and paper.
Performing this process on a computer amounts to no more than mere instructions to apply.
Claim 33 recites “wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for creating a frequency score, to describe the total stacked defect area above a chosen threshold in comparison to the total defect area, calculated as {EQUATION} where nievels is the total number of defect levels in the laminate and Alevel is the area of the level.”
Calculating this frequency score using such an equation is merely a mathematic calculation, and therefore a mathematic concept.
Performing this process on a computer amounts to no more than mere instructions to apply.
Claim 34 recites “wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for creating a severity to compare defect levels above the chosen threshold to total defect level area, calculated as: {EQUATION}”
Calculating this severity score using such an equation is merely a mathematic calculation, and therefore a mathematic concept.
Performing this process on a computer amounts to no more than mere instructions to apply.
Claim 35 recites “wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for calculating a single score that combines frequency and severity of each defect through use of an analytic hierarchy process (AHP) matrix.”
Calculating this combined score using such an equation involving a matrix is merely a mathematic calculation, and therefore a mathematic concept.
Performing this process on a computer amounts to no more than mere instructions to apply.
Claim 36 recites “wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for converting the matrix into rankings used to compute the score of each scenario, based on user selected relative values within the matrix designated per the user's relative priorities for certain defects.”
Obtaining selected values from a user, when recited at such a high level of generality, amounts to no more than mere data gathering.
Computing the score each scenario and ranking them is a combination mathematic/mental process equivalent to first mathematically calculating each score then mentally sorting these scores into an ordered list.
Performing this process on a computer amounts to no more than mere instructions to apply.
Claim 37 recites “wherein said one or more processors are further programmed for conducting computer-Aided Process Planning (CAPP) for computing individual scenario scores, calculated as: {EQUATION}”
Calculating such scores using a mathematic equation amounts to no more than a mathematic calculation, and is therefore merely a mathematic concept.
Performing this process on a computer amounts to no more than mere instructions to apply.
Claim 38 recites “wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for computing manufacturability for the entire laminate as a function of the size of each ply, where the score is the maximum score from all scenarios in the associated ply, the manufacturability calculated as: {EQUATION} where a manufacturability score of 1 is the highest possible value and 0 is the lowest.”
Calculating such a score using a mathematic equation amounts to no more than a mathematic calculation, and is therefore merely a mathematic concept.
Performing this process on a computer amounts to no more than mere instructions to apply.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 and 7-13 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Automation of Process Planning for Automated Fiber Placement (Hereinafter Halbritter_2020).
Claim 1. Halbritter_2020 anticipates Methodology for process planning for Automated Fiber Placement (AFP) manufacturing for identifying optimal starting point location and layup strategy for each ply of a subject laminate, for producing complex structures and large structures, comprising: providing one or more processors programmed for conducting Computer-Aided Process Planning (CAPP), wherein said one or more processors are programmed for iteratively determining:
([Abstract] “Process planning represents an essential stage of the Automated Fiber Placement (AFP) workflow. It develops useful and efficient machine processes based upon the working material, composite design, and manufacturing resources. …This document presents an automated software solution to the layup strategy and starting point selection phase of Process Planning. A series of ply scenarios are generated with variations of these ply parameters and evaluated according to a set of metrics entered by the Process Planner. These metrics are generated through use of the Analytical Hierarchy Process (AHP), where relative importance between each of the fiber features are defined. The ply scenarios are selected which reduce the overall fiber feature scores based on the defects the Process Planner wishes to minimize.” [Page 77 Par 1] “The goal of the Computer Aided Processes Planning (CAPP) software is to implement and appropriately automate the process planning functions identified in the down selection process of Chapter 4 and further expanded upon in Chapter 5. The following chapter discusses the creation of the CAPP software and how it aids in the rapid prototyping design phase of composite laminates along with the ply level optimization to reduce geometry related fiber defects. The methods laid out will ultimately help find the ideal starting points and layup strategies to reduce the fiber defects.” [Page 62 Par 3] “This chapter will define the techniques used for measuring the fiber defects and how they are used to create the objective function. Following this, the iterative method used to approach an optimized ply using the objective function is described.” [Figure 6.1] Shows the flow of software control and the optimization process)
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(1) a plurality of ply scenarios by locating respective starting points and associated layup strategies and subsequently presenting resulting geometrical fiber defect instances and severity measurements, ([Page 68 Par 3 – Page 69 Par 3] “5.2 DEFECT INSTANCES AND SEVERITY Process planning works to minimize defects and unwanted features that will develop based on the chosen process planning parameters. Currently, there are techniques in place that are able to measure these defects in a case by case basis but lack the ability to summarize the defects on a ply-wide scale. Additionally, there is no system to compare the presence of defects on a common scale where they may be used to generate a logical, overall ranking for a generated scenario depending on the requirements set forth by the process planner. To summarize these features, two aspects of their presence must be considered. The majority of manufacturing and design processes set a maximum instance threshold for each feature. An instance threshold measures total amount of defects above a certain threshold. These limitations are imposed to reduce the propagation of more defects and to improve the manufacturability of the laminate. The other measurement used by process planners is severity. The severity threshold of the defect finds the amount that the instances above the threshold value affect the total amount of defects. This measures how significantly certain defects will affect the overall laminate and will demonstrate if there are many small defects or several large defects. The Instance functions as an indicator of commonality of the features for the given ply. The Severity of the feature uses the threshold to find the sum of the instances as they occur in the ply.” [Page 82 Par 2 – Page 83 Par 1] “6.3 CREATION OF PLY SCENARIOS The CAPP’s initial function is to find the starting points for different tools and boundaries and then export these to the Vericut Composites Program (VCP) software. As regularly stated in section 4.1.2, finding the correct starting point is a critical function of process planning. Finding the correct starting point can be a tedious and difficult task that benefits greatly from automation. To help reduce the time for this task the CAPP module finds an array of starting points for each ply.”) (2) defining the relative importance of defect types to create an overall ranking of the defect set that is used for ply level optimization, and ([Page 73 Par 6 – Page 74 Par 1] “The Analytical Hierarchy Process (AHP) provides a method for creating an overall ranking of many features through a series of pair-wise comparisons. These comparisons are then used to develop a relative weight for each of the fiber defect variations. The value of relative importance between each pair of defects were entered into the upper half of the matrix and signifies how much the column is preferred over the row criteria. By doing this, the bottom half of the matrix is automatically computed as the inverse values of the upper half as seen in Figure 5.13a. From there, the sum of each column is computed and divides the value in each column to achieve Figure 5.13b. The final weights of Figure 5.14 are achieve by average the values of each column in Figure 5.13b. These final weights represent the relative importance of each type of defect and are utilized in the objective function.” [Page 75 Par 2] “The objective function uses the data that is defined in sections 5.2 and 5.3. The summary of the ply’s defects and the selection of the defects to prioritize are combined to create a normalized value quantifying each ply. The chosen rankings apply weights to our defects, such that the most highly ranked defects impart the most to the overall value. Therefore, it is necessary to minimize our objective function in order to produce the ply with the lowest amount of the prioritized defects.”) (3) determining final scores for each ply scenario, so that ply level defects are combined to determine optimal laminate construction that reduces the buildup of defects through the thickness of the laminate. ([Page 75 Par 1 – Page 76 Par 1] “Due to the time required to compute the ply from the specified inputs, it is not currently feasible to test every possible pair of inputs, so instead an iterative approach is used. Each ply is initially defined by a ply boundary and ply angle. These provide the basis for the defined plies of the laminate; therefore, the ply boundary and ply angle can be used to create several variations of each ply. These variations of inputs for each ply create what is known here as ply scenarios and serve as the inputs to our objective function. The objective function uses the data that is defined in sections 5.2 and 5.3. The summary of the ply’s defects and the selection of the defects to prioritize are combined to create a normalized value quantifying each ply. The chosen rankings apply weights to our defects, such that the most highly ranked defects impart the most to the overall value. Therefore, it is necessary to minimize our objective function in order to produce the ply with the lowest amount of the prioritized defects. The first step in the optimization begins by creating a localized grid of points at the HKS center of the geometry as in Figure 5.15a. The grid points are oriented with the specified ply angle and are initially spaced by half a tow width. Each grid point represents a starting point for a different ply scenario, and each point may utilize several different layup strategies. Each of these ply scenarios are processed and scored using the defined objective function. Following iterations, ply scenarios, as in Figure 5.15b, are generated using the starting point and layup strategy with the lowest score. Iteration to the optimal ply solution can be stopped after reaching an acceptable number of defects, or when a certain amount of time has elapsed in computing each round of ply scenarios.”
Claim 7. Halbritter_2020 anticipates wherein the resulting geometrical fiber defect instances and severity measurements are presented using a communication with VERICUT Composite Programming. ([Page 68 Par 3 – Page 69 Par 3] “5.2 DEFECT INSTANCES AND SEVERITY Process planning works to minimize defects and unwanted features that will develop based on the chosen process planning parameters. Currently, there are techniques in place that are able to measure these defects in a case by case basis but lack the ability to summarize the defects on a ply-wide scale. Additionally, there is no system to compare the presence of defects on a common scale where they may be used to generate a logical, overall ranking for a generated scenario depending on the requirements set forth by the process planner. To summarize these features, two aspects of their presence must be considered. The majority of manufacturing and design processes set a maximum instance threshold for each feature. An instance threshold measures total amount of defects above a certain threshold. These limitations are imposed to reduce the propagation of more defects and to improve the manufacturability of the laminate. The other measurement used by process planners is severity. The severity threshold of the defect finds the amount that the instances above the threshold value affect the total amount of defects. This measures how significantly certain defects will affect the overall laminate and will demonstrate if there are many small defects or several large defects. The Instance functions as an indicator of commonality of the features for the given ply. The Severity of the feature uses the threshold to find the sum of the instances as they occur in the ply.” [Page 82 Par 2 – Page 83 Par 1] “6.3 CREATION OF PLY SCENARIOS The CAPP’s initial function is to find the starting points for different tools and boundaries and then export these to the Vericut Composites Program (VCP) software. As regularly stated in section 4.1.2, finding the correct starting point is a critical function of process planning. Finding the correct starting point can be a tedious and difficult task that benefits greatly from automation. To help reduce the time for this task the CAPP module finds an array of starting points for each ply.”)
Claim 8. Halbritter_2020 anticipates wherein the geometrical fiber defect instances comprise fiber gaps, overlaps, angle deviation, and degree of steering. ([Page 68 Par 3 – Page 69 Par 3] “5.2 DEFECT INSTANCES AND SEVERITY Process planning works to minimize defects and unwanted features that will develop based on the chosen process planning parameters. …. To summarize these features, two aspects of their presence must be considered. The majority of manufacturing and design processes set a maximum instance threshold for each feature. An instance threshold measures total amount of defects above a certain threshold. These limitations are imposed to reduce the propagation of more defects and to improve the manufacturability of the laminate. The other measurement used by process planners is severity. The severity threshold of the defect finds the amount that the instances above the threshold value affect the total amount of defects. This measures how significantly certain defects will affect the overall laminate and will demonstrate if there are many small defects or several large defects. The Instance functions as an indicator of commonality of the features for the given ply. The Severity of the feature uses the threshold to find the sum of the instances as they occur in the ply.” [Page 70 Par 2] “The Instance and Severity measurements function together to provide a common scale to measure instances that occur beyond a set threshold value. The following paragraphs will detail the 4 selected features (Gap, Overlap, Angle Deviation and Steering). For each, a clear definition is presented with respect to the instances and severity.”)
Claim 9. Halbritter_2020 anticipates wherein the final scores are ranked for each ply scenario organized by starting point and the chosen layup strategy, chosen so that ply level defects are combined to determine the optimal laminate construction that reduces the buildup of defects through the thickness of the laminate, so that process planners create fiber paths that mitigate the number of geometry-based defects and the compounding effect they have throughout a laminate. ([Page 73 Par 5] “COMPARISON AND RANKING The Analytical Hierarchy Process (AHP) provides a method for creating an overall ranking of many features through a series of pair-wise comparisons. These comparisons are then used to develop a relative weight for each of the fiber defect variations.” [Page 76 Par 2] “The methods set forth in this chapter enable the quantification and optimization of ply scenarios. This quantification was a function of both the resultant geometrical defects from the layup process and the rankings set by the process planner. The geometrical defects were measured as functions of their instances and severity, where the combinations of both these measures were meant to summarize the extent of the defects. These measurements were combined with the one-to-one ranking process enabled by the AHP and resulted in single normalized scores for each ply scenario.” [Page 77 Par 2] “The CAPP software is broken down into three major functions. First, the software helps to create several ply scenarios by locating starting points with potential layup strategies and presents the resulting geometrical fiber defect instance and severity measurements. The second portion allows the process planner to define the relative importance of defect types in order to create an overall ranking of the defect set that is used for the ply level optimization. The final function presents final scores for each ply scenario and organizes them by starting point and the chosen layup strategy. These scores can be used to decide if a satisfactory solution has been reached or if additional iterations should be performed. Minimizing the ply’s overall score, or objective function, indicates that a solution has been reached which adequately minimizes the prioritized defect types.” [Page 67 Par 3] “The use of fiber steering (Figure 5.9) allows for optimization of laminate loading properties in addition to altering laminates to avoid defects due to the presence of certain tool geometries. Fiber steering achieves these effects by changing the in-plane curvature of the fiber path from the classic linear orthogonal laminates.” [Page 5 Par 2] “LAYUP STRATEGY CONCEPTS This section covers the various concepts regarding the geometry of fiber paths that can be utilized to generate surface coverage. These geometrical concepts define the interaction of the thin and stiff material of the fiber tows being placed on a potentially complex geometrical tool surface.”)
Claim 10. Halbritter_2020 anticipates A down selection process of fiber placement strategies and seed points for Automated Fiber Placement (AFP) manufacturing in order to independently generate optimized plies to be used together for a composite structure to be AFP manufactured, the process comprising: ([Abstract] “Process planning represents an essential stage of the Automated Fiber Placement (AFP) workflow. It develops useful and efficient machine processes based upon the working material, composite design, and manufacturing resources. …This document presents an automated software solution to the layup strategy and starting point selection phase of Process Planning. A series of ply scenarios are generated with variations of these ply parameters and evaluated according to a set of metrics entered by the Process Planner. These metrics are generated through use of the Analytical Hierarchy Process (AHP), where relative importance between each of the fiber features are defined. The ply scenarios are selected which reduce the overall fiber feature scores based on the defects the Process Planner wishes to minimize.” [Page 77 Par 1] “The goal of the Computer Aided Processes Planning (CAPP) software is to implement and appropriately automate the process planning functions identified in the down selection process of Chapter 4 and further expanded upon in Chapter 5. The following chapter discusses the creation of the CAPP software and how it aids in the rapid prototyping design phase of composite laminates along with the ply level optimization to reduce geometry related fiber defects. The methods laid out will ultimately help find the ideal starting points and layup strategies to reduce the fiber defects.”) evaluating and predicting geometrically defined fiber defects of a plurality of possible ply geometries for use in the composite structure; and ([Page 68 Par 3 – Page 69 Par 3] “5.2 DEFECT INSTANCES AND SEVERITY Process planning works to minimize defects and unwanted features that will develop based on the chosen process planning parameters. Currently, there are techniques in place that are able to measure these defects in a case by case basis but lack the ability to summarize the defects on a ply-wide scale. Additionally, there is no system to compare the presence of defects on a common scale where they may be used to generate a logical, overall ranking for a generated scenario depending on the requirements set forth by the process planner. To summarize these features, two aspects of their presence must be considered. The majority of manufacturing and design processes set a maximum instance threshold for each feature. An instance threshold measures total amount of defects above a certain threshold. These limitations are imposed to reduce the propagation of more defects and to improve the manufacturability of the laminate. The other measurement used by process planners is severity. The severity threshold of the defect finds the amount that the instances above the threshold value affect the total amount of defects. This measures how significantly certain defects will affect the overall laminate and will demonstrate if there are many small defects or several large defects. The Instance functions as an indicator of commonality of the features for the given ply. The Severity of the feature uses the threshold to find the sum of the instances as they occur in the ply.” [Page 82 Par 2 – Page 83 Par 1] “6.3 CREATION OF PLY SCENARIOS The CAPP’s initial function is to find the starting points for different tools and boundaries and then export these to the Vericut Composites Program (VCP) software. As regularly stated in section 4.1.2, finding the correct starting point is a critical function of process planning. Finding the correct starting point can be a tedious and difficult task that benefits greatly from automation. To help reduce the time for this task the CAPP module finds an array of starting points for each ply.” [Page 73 Par 6 – Page 74 Par 1] “The Analytical Hierarchy Process (AHP) provides a method for creating an overall ranking of many features through a series of pair-wise comparisons. These comparisons are then used to develop a relative weight for each of the fiber defect variations. The value of relative importance between each pair of defects were entered into the upper half of the matrix and signifies how much the column is preferred over the row criteria. By doing this, the bottom half of the matrix is automatically computed as the inverse values of the upper half as seen in Figure 5.13a. From there, the sum of each column is computed and divides the value in each column to achieve Figure 5.13b. The final weights of Figure 5.14 are achieve by average the values of each column in Figure 5.13b. These final weights represent the relative importance of each type of defect and are utilized in the objective function.”) performing an iterative optimization of the possible respective ply geometries as a function of the placement strategy and seed point, where the optimization minimizes impact of resulting fiber defects in the composite structure. ([Page 75 Par 1 – Page 76 Par 1] “Due to the time required to compute the ply from the specified inputs, it is not currently feasible to test every possible pair of inputs, so instead an iterative approach is used. Each ply is initially defined by a ply boundary and ply angle. These provide the basis for the defined plies of the laminate; therefore, the ply boundary and ply angle can be used to create several variations of each ply. These variations of inputs for each ply create what is known here as ply scenarios and serve as the inputs to our objective function. The objective function uses the data that is defined in sections 5.2 and 5.3. The summary of the ply’s defects and the selection of the defects to prioritize are combined to create a normalized value quantifying each ply. The chosen rankings apply weights to our defects, such that the most highly ranked defects impart the most to the overall value. Therefore, it is necessary to minimize our objective function in order to produce the ply with the lowest amount of the prioritized defects. The first step in the optimization begins by creating a localized grid of points at the HKS center of the geometry as in Figure 5.15a. The grid points are oriented with the specified ply angle and are initially spaced by half a tow width. Each grid point represents a starting point for a different ply scenario, and each point may utilize several different layup strategies. Each of these ply scenarios are processed and scored using the defined objective function. Following iterations, ply scenarios, as in Figure 5.15b, are generated using the starting point and layup strategy with the lowest score. Iteration to the optimal ply solution can be stopped after reaching an acceptable number of defects, or when a certain amount of time has elapsed in computing each round of ply scenarios.” [Page 84 Par 5] “Each starting point will have a specific layup strategy associated with it. These layup strategies are created in VCP and use the principles set out in Chapter 2. The process planner will have the option of choosing which layup strategy they want each time they create a starting point array.” [Page 90 Par 2 – Page 91 Par 2] “Major 1 has generated a normalized set of instance and severity measurements for each feature of interest. Major 2 provides a method for creating an overall ranking of many features with a series of pair-wise comparisons through the Analytical Hierarchy process. Major 3 combines the data into a single normalized score for each ply scenario. The lowest score will identify the best starting point and layup strategy of that iteration according to the thresholds and preferences used. Creating the array of starting points earlier will help the process planner as a variety of potential starting points can be easily compared to each other for each ply. The display for Major 3 can be seen in Figure 6.18. The intuitive display clearly shows which starting points are the best by color coding them on a scale from red, being worst, to green, being best. When creating the next iteration of starting points, the CAPP module will select the starting point and strategy with the lowest score. Through multiple iterations the best scenario can be located with minimal effort.”)
Claim 11. Halbritter_2020 anticipates wherein the geometrically defined fiber defects include at least one of fiber gaps, overlaps, angle deviation, and degree of steering. ([Page 68 Par 3 – Page 69 Par 3] “5.2 DEFECT INSTANCES AND SEVERITY Process planning works to minimize defects and unwanted features that will develop based on the chosen process planning parameters. …. To summarize these features, two aspects of their presence must be considered. The majority of manufacturing and design processes set a maximum instance threshold for each feature. An instance threshold measures total amount of defects above a certain threshold. These limitations are imposed to reduce the propagation of more defects and to improve the manufacturability of the laminate. The other measurement used by process planners is severity. The severity threshold of the defect finds the amount that the instances above the threshold value affect the total amount of defects. This measures how significantly certain defects will affect the overall laminate and will demonstrate if there are many small defects or several large defects. The Instance functions as an indicator of commonality of the features for the given ply. The Severity of the feature uses the threshold to find the sum of the instances as they occur in the ply.” [Page 70 Par 2] “The Instance and Severity measurements function together to provide a common scale to measure instances that occur beyond a set threshold value. The following paragraphs will detail the 4 selected features (Gap, Overlap, Angle Deviation and Steering). For each, a clear definition is presented with respect to the instances and severity.”)
Claim 12. Halbritter_2020 anticipates wherein the placement strategy and seed point corresponds with the starting point and the chosen layup strategy for each respective ply geometry. ([Abstract] “Process planning represents an essential stage of the Automated Fiber Placement (AFP) workflow. It develops useful and efficient machine processes based upon the working material, composite design, and manufacturing resources. …This document presents an automated software solution to the layup strategy and starting point selection phase of Process Planning. A series of ply scenarios are generated with variations of these ply parameters and evaluated according to a set of metrics entered by the Process Planner. These metrics are generated through use of the Analytical Hierarchy Process (AHP), where relative importance between each of the fiber features are defined. The ply scenarios are selected which reduce the overall fiber feature scores based on the defects the Process Planner wishes to minimize.” [Page 77 Par 1] “The goal of the Computer Aided Processes Planning (CAPP) software is to implement and appropriately automate the process planning functions identified in the down selection process of Chapter 4 and further expanded upon in Chapter 5. The following chapter discusses the creation of the CAPP software and how it aids in the rapid prototyping design phase of composite laminates along with the ply level optimization to reduce geometry related fiber defects. The methods laid out will ultimately help find the ideal starting points and layup strategies to reduce the fiber defects.” [Page 62 Par 3] “This chapter will define the techniques used for measuring the fiber defects and how they are used to create the objective function. Following this, the iterative method used to approach an optimized ply using the objective function is described.” [Page 62 Par 1-2] “PLY LEVEL OPTIMIZATION The creation of the best layups can be approached as an optimization problem. In order to perform this optimization, it is necessary to quantify and score the layups generated with the strategies defined in Chapter 2. For this approach, a ply level optimization is performed, where each ply is individually tailored in order to minimize its objective function. The objective function allows us to quantify the resulting plies according to the following sets of inputs and outputs. The inputs will be two of the functions defined in Chapter 4, the starting point (section 4.1.2) and the chosen layup strategy (section 4.1.4). These are two of the functions that were identified from the process planning survey as benefiting from some automation, additionally these have a very direct effect on the resulting ply. The outputs are the defects directly resulting from the geometrical relationship between the tool surface and the created plies. The defects chosen here were gaps and overlaps (section 3.2.1), angle deviation (section 3.2.6), and fiber steering. Fiber steering can be directly measured from the geometry of the created fiber paths and can lead to several other defects: puckers (section 3.2.2), wrinkles (3.2.3), and folds (3.2.7).“ [Figure 6.1] Shows the flow of software control and the optimization process)
Claim 13. Halbritter_2020 anticipates wherein the optimization minimizes the frequency and severity of where defects are stacking on top of each other in the composite structure. ([Abstract] “Process planning represents an essential stage of the Automated Fiber Placement (AFP) workflow. It develops useful and efficient machine processes based upon the working material, composite design, and manufacturing resources. …This document presents an automated software solution to the layup strategy and starting point selection phase of Process Planning. A series of ply scenarios are generated with variations of these ply parameters and evaluated according to a set of metrics entered by the Process Planner. These metrics are generated through use of the Analytical Hierarchy Process (AHP), where relative importance between each of the fiber features are defined. The ply scenarios are selected which reduce the overall fiber feature scores based on the defects the Process Planner wishes to minimize.” [Page 77 Par 1] “The goal of the Computer Aided Processes Planning (CAPP) software is to implement and appropriately automate the process planning functions identified in the down selection process of Chapter 4 and further expanded upon in Chapter 5. The following chapter discusses the creation of the CAPP software and how it aids in the rapid prototyping design phase of composite laminates along with the ply level optimization to reduce geometry related fiber defects. The methods laid out will ultimately help find the ideal starting points and layup strategies to reduce the fiber defects.” [Page 62 Par 1-2] “PLY LEVEL OPTIMIZATION The creation of the best layups can be approached as an optimization problem. In order to perform this optimization, it is necessary to quantify and score the layups generated with the strategies defined in Chapter 2. For this approach, a ply level optimization is performed, where each ply is individually tailored in order to minimize its objective function. The objective function allows us to quantify the resulting plies according to the following sets of inputs and outputs. The inputs will be two of the functions defined in Chapter 4, the starting point (section 4.1.2) and the chosen layup strategy (section 4.1.4). These are two of the functions that were identified from the process planning survey as benefiting from some automation, additionally these have a very direct effect on the resulting ply. The outputs are the defects directly resulting from the geometrical relationship between the tool surface and the created plies. The defects chosen here were gaps and overlaps (section 3.2.1), angle deviation (section 3.2.6), and fiber steering. Fiber steering can be directly measured from the geometry of the created fiber paths and can lead to several other defects: puckers (section 3.2.2), wrinkles (3.2.3), and folds (3.2.7)… This chapter will define the techniques used for measuring the fiber defects and how they are used to create the objective function. Following this, the iterative method used to approach an optimized ply using the objective function is described.” [Page 68 Par 3 – Page 69 Par 3] “Process planning works to minimize defects and unwanted features that will develop based on the chosen process planning parameters. … To summarize these features, two aspects of their presence must be considered. The majority of manufacturing and design processes set a maximum instance threshold for each feature. An instance threshold measures total amount of defects above a certain threshold. These limitations are imposed to reduce the propagation of more defects and to improve the manufacturability of the laminate. The other measurement used by process planners is severity. The severity threshold of the defect finds the amount that the instances above the threshold value affect the total amount of defects. This measures how significantly certain defects will affect the overall laminate and will demonstrate if there are many small defects or several large defects. The Instance functions as an indicator of commonality of the features for the given ply. The Severity of the feature uses the threshold to find the sum of the instances as they occur in the ply.” [Page 33 Par 1-2] “A gap occurs when two adjacent tows are not perfectly laid up adjacent to each other resulting in a gap between the tows. An overlap is when the two adjacent tows are overlapping onto each other. The most common cause of gaps and overlaps is steering during layup since, the tows in a course will not fit together perfectly, especially when adopting a parallel coverage strategy. However, gaps and overlaps can naturally occur outside of steering if laying up over a complex 3D tool surface. Gaps and overlaps are well anticipated in areas of purposeful steering, or on doubly curved surfaces where adequate roller compaction is not guaranteed. From the geometry of these situations, the gaps and overlaps can be directly computed. The measurement of gaps and overlaps are generally easy to detect due to the build-up or lack of material along the course. As successive plies are laid up over overlaps, consistently at the same location at each layer, significant thickness buildup will be visible. A gap and/or overlap may become a site for failure initiation under loads. Gaps would create resin rich regions for crack growth while overlaps create undulation in the fiber that can lead to compressive failures. They may also become a site for wrinkling in the layers placed over them in the succeeding layer.” [Page 79 Par 1] “The flow diagram presents the CAPP’s relationship with the other software and which types of data are shared during each step to achieve the laminate optimization. The process begins with the CO, which passes the basic laminate definitions to the CAPP. These laminate definitions contain the tool surface on which the laminate will be constructed as well as the ply boundary and ply angles that define each individual ply in that laminate. The CAPP takes this laminate definition and constructs and enters the ply level optimization loop with VCP. Here, different laminates are iterated upon to minimize the prioritized defects. VCP enables this iteration by building the geometry of the ply scenarios supplied by the CAPP and identifying the resulting geometric defects. After a sufficiently optimized laminate has been generated, the process planning results can be used to continue the laminate design phase.”)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
(1) Claims 2-3, 14-19, 21-23, 26-27, 30, and 32 are rejected under 35 U.S.C. 103 as being unpatentable over Automation of Process Planning for Automated Fiber Placement (Hereinafter Halbritter_2020) in view of Development of a Design for Manufacturing Tool for Automated Fiber Placement Structure (Hereinafter Noevere)
Claim 2. Halbritter_2020 teaches wherein said one or more processors are programmed for optimizing the selection of ply scenario input parameters ([Abstract] “Process planning represents an essential stage of the Automated Fiber Placement (AFP) workflow. It develops useful and efficient machine processes based upon the working material, composite design, and manufacturing resources. …This document presents an automated software solution to the layup strategy and starting point selection phase of Process Planning. A series of ply scenarios are generated with variations of these ply parameters and evaluated according to a set of metrics entered by the Process Planner. These metrics are generated through use of the Analytical Hierarchy Process (AHP), where relative importance between each of the fiber features are defined. The ply scenarios are selected which reduce the overall fiber feature scores based on the defects the Process Planner wishes to minimize.” [Page 77 Par 1] “The goal of the Computer Aided Processes Planning (CAPP) software is to implement and appropriately automate the process planning functions identified in the down selection process of Chapter 4 and further expanded upon in Chapter 5. The following chapter discusses the creation of the CAPP software and how it aids in the rapid prototyping design phase of composite laminates along with the ply level optimization to reduce geometry related fiber defects. The methods laid out will ultimately help find the ideal starting points and layup strategies to reduce the fiber defects.” [Page 62 Par 3] “This chapter will define the techniques used for measuring the fiber defects and how they are used to create the objective function. Following this, the iterative method used to approach an optimized ply using the objective function is described.” [Figure 6.1] Shows the flow of software control and the optimization process)
Halbritter_2020 does not explicitly teach optimizing the selection of ply scenario input parameters using surrogate-based methods.
Noevere makes obvious optimizing the selection of ply scenario input parameters using surrogate-based methods. ([Page 6 Par 14] “The initial model presented in Ref. [3] performs simulations on flat tooling. In this mode of operation, the model will be used to generate a surrogate model (via polynomial regression) that estimates the probability of AFP defects as a function of steering radius and other process parameters.” [Page 10 Par 2-5] “Surrogate models are often used to represent time-consuming higher fidelity analyses (also known as response surface models). Polynomial regression was used to generate the surrogate model for the flat surface AFP defect simulation. … The exponents 𝐸𝑖 are defined by the selected form of the polynomial. The coefficients 𝐶𝑖 are generated when regression is performed against a sensitivity analysis of AFP process parameters. After the sensitivity analysis of process parameters has been run through the AFP defects simulation described previously, the probability of AFP defects is extracted from the simulation results. These datapoints are used in least squares regression to produce the coefficients described above. … The surrogate model is utilized in the Central Optimizer process to evaluate steering radii predicted by VCP. VCP can export steering radii for each ply across the entirety of the tool surface. Each radius is evaluated with the surrogate model to produce a probability map of defect likelihood across the entire tool surface, for each ply.”)
Noevere is analogous art because it is within the field of automated fiber placement optimization. It would have been obvious to one of ordinary skill in the art to combine Noevere with Halbritter_2020 in order to more efficiently perform analysis of the various generated configurations. Halbritter_2020 notes the excessive processing time required for analyzing an individual ply using conventional methods ([Page 75 Par 1] “… Due to the time required to compute the ply from the specified inputs, it is not currently feasible to test every possible pair of inputs …”) To this end Noevere uses surrogate modelling to perform ply-level analysis, resulting in significantly more efficient modelling, noting how surrogate models are a common method for efficiently representing otherwise time-consuming analyses. ([Page 10 Par 2] “Surrogate models are often used to represent time-consuming higher fidelity analyses (also known as response surface models). Polynomial regression was used to generate the surrogate model for the flat surface AFP defect simulation.”) Overall, one of ordinary skill in the art would have recognized that combining Halbritter_2020 with Noevere would result in a significantly more efficient modeling system, allowing optimized designs to be achieved more quickly and efficiently.
Claim 3. Halbritter_2020 teaches wherein said input parameters include at least one of starting points, layup strategies, and tows per course. ([Page 62 Par 1-2] “PLY LEVEL OPTIMIZATION The creation of the best layups can be approached as an optimization problem. In order to perform this optimization, it is necessary to quantify and score the layups generated with the strategies defined in Chapter 2. For this approach, a ply level optimization is performed, where each ply is individually tailored in order to minimize its objective function. The objective function allows us to quantify the resulting plies according to the following sets of inputs and outputs. The inputs will be two of the functions defined in Chapter 4, the starting point (section 4.1.2) and the chosen layup strategy (section 4.1.4).”)
Claim 14. Noevere teaches wherein defects are minimized by optimizing the selection of input parameters using surrogate-based methods. ([Page 6 Par 14] “The initial model presented in Ref. [3] performs simulations on flat tooling. In this mode of operation, the model will be used to generate a surrogate model (via polynomial regression) that estimates the probability of AFP defects as a function of steering radius and other process parameters.” [Page 10 Par 2-5] “Surrogate models are often used to represent time-consuming higher fidelity analyses (also known as response surface models). Polynomial regression was used to generate the surrogate model for the flat surface AFP defect simulation. … The exponents 𝐸𝑖 are defined by the selected form of the polynomial. The coefficients 𝐶𝑖 are generated when regression is performed against a sensitivity analysis of AFP process parameters. After the sensitivity analysis of process parameters has been run through the AFP defects simulation described previously, the probability of AFP defects is extracted from the simulation results. These datapoints are used in least squares regression to produce the coefficients described above. … The surrogate model is utilized in the Central Optimizer process to evaluate steering radii predicted by VCP. VCP can export steering radii for each ply across the entirety of the tool surface. Each radius is evaluated with the surrogate model to produce a probability map of defect likelihood across the entire tool surface, for each ply.”)
Noevere is analogous art because it is within the field of automated fiber placement optimization. It would have been obvious to one of ordinary skill in the art to combine Noevere with Halbritter_2020 in order to more efficiently perform analysis of the various generated configurations. Halbritter_2020 notes the excessive processing time required for analyzing an individual ply using conventional methods ([Page 75 Par 1] “… Due to the time required to compute the ply from the specified inputs, it is not currently feasible to test every possible pair of inputs …”) To this end Noevere uses surrogate modelling to perform ply-level analysis, resulting in significantly more efficient modelling, noting how surrogate models are a common method for efficiently representing otherwise time-consuming analyses. ([Page 10 Par 2] “Surrogate models are often used to represent time-consuming higher fidelity analyses (also known as response surface models). Polynomial regression was used to generate the surrogate model for the flat surface AFP defect simulation.”) Overall, one of ordinary skill in the art would have recognized that combining Halbritter_2020 with Noevere would result in a significantly more efficient modeling system, allowing optimized designs to be achieved more quickly and efficiently.
Claim 15. Halbritter_2020 teaches wherein defects are minimized prior to manufacture of the composite structure by optimizing the selection of at least one of starting points, layup strategies, and tows per course. ([Page 2 Par 3] “The economic impacts of process planning for AFP arise from the reduction in time it takes to program the machine prior to manufacturing by embedding design for manufacturing concepts well ahead in the product lifecycle.” [Page 62 Par 1-2] “PLY LEVEL OPTIMIZATION The creation of the best layups can be approached as an optimization problem. In order to perform this optimization, it is necessary to quantify and score the layups generated with the strategies defined in Chapter 2. For this approach, a ply level optimization is performed, where each ply is individually tailored in order to minimize its objective function. The objective function allows us to quantify the resulting plies according to the following sets of inputs and outputs. The inputs will be two of the functions defined in Chapter 4, the starting point (section 4.1.2) and the chosen layup strategy (section 4.1.4).” [Page 32 Par 2] “The ability to model these defects enables an entire virtual layup to be built and analyzed before any manufacturing. The details of step optimization process are defined in Chapter 5.”)
Claim 16. Halbritter_2020 teaches A process planning methodology for Automated Fiber Placement (AFP) for mitigating gap and overlap defect stacking for resulting uniform through-thickness laminate, said methodology comprising: ([Abstract] “Process planning represents an essential stage of the Automated Fiber Placement (AFP) workflow. It develops useful and efficient machine processes based upon the working material, composite design, and manufacturing resources. …This document presents an automated software solution to the layup strategy and starting point selection phase of Process Planning. A series of ply scenarios are generated with variations of these ply parameters and evaluated according to a set of metrics entered by the Process Planner. These metrics are generated through use of the Analytical Hierarchy Process (AHP), where relative importance between each of the fiber features are defined. The ply scenarios are selected which reduce the overall fiber feature scores based on the defects the Process Planner wishes to minimize.” [Page 77 Par 1] “The goal of the Computer Aided Processes Planning (CAPP) software is to implement and appropriately automate the process planning functions identified in the down selection process of Chapter 4 and further expanded upon in Chapter 5. The following chapter discusses the creation of the CAPP software and how it aids in the rapid prototyping design phase of composite laminates along with the ply level optimization to reduce geometry related fiber defects. The methods laid out will ultimately help find the ideal starting points and layup strategies to reduce the fiber defects.” [Page 62 Par 3] “This chapter will define the techniques used for measuring the fiber defects and how they are used to create the objective function. Following this, the iterative method used to approach an optimized ply using the objective function is described.” [Page 62 Par 1-2] “PLY LEVEL OPTIMIZATION The creation of the best layups can be approached as an optimization problem. In order to perform this optimization, it is necessary to quantify and score the layups generated with the strategies defined in Chapter 2. For this approach, a ply level optimization is performed, where each ply is individually tailored in order to minimize its objective function. The objective function allows us to quantify the resulting plies according to the following sets of inputs and outputs. The inputs will be two of the functions defined in Chapter 4, the starting point (section 4.1.2) and the chosen layup strategy (section 4.1.4). These are two of the functions that were identified from the process planning survey as benefiting from some automation, additionally these have a very direct effect on the resulting ply. The outputs are the defects directly resulting from the geometrical relationship between the tool surface and the created plies. The defects chosen here were gaps and overlaps (section 3.2.1), angle deviation (section 3.2.6), and fiber steering. Fiber steering can be directly measured from the geometry of the created fiber paths and can lead to several other defects: puckers (section 3.2.2), wrinkles (3.2.3), and folds (3.2.7).“ [Figure 6.1] Shows the flow of software control and the optimization process) providing one or more processors programmed for: using ([Page 75 Par 1 – Page 76 Par 1] “ITERATIVE OPTIMIZATION APPROACH Due to the time required to compute the ply from the specified inputs, it is not currently feasible to test every possible pair of inputs, so instead an iterative approach is used. Each ply is initially defined by a ply boundary and ply angle. These provide the basis for the defined plies of the laminate; therefore, the ply boundary and ply angle can be used to create several variations of each ply. These variations of inputs for each ply create what is known here as ply scenarios and serve as the inputs to our objective function. The objective function uses the data that is defined in sections 5.2 and 5.3. The summary of the ply’s defects and the selection of the defects to prioritize are combined to create a normalized value quantifying each ply. The chosen rankings apply weights to our defects, such that the most highly ranked defects impart the most to the overall value. Therefore, it is necessary to minimize our objective function in order to produce the ply with the lowest amount of the prioritized defects. The first step in the optimization begins by creating a localized grid of points at the HKS center of the geometry as in Figure 5.15a. The grid points are oriented with the specified ply angle and are initially spaced by half a tow width. Each grid point represents a starting point for a different ply scenario, and each point may utilize several different layup strategies. Each of these ply scenarios are processed and scored using the defined objective function. Following iterations, ply scenarios, as in Figure 5.15b, are generated using the starting point and layup strategy with the lowest score. Iteration to the optimal ply solution can be stopped after reaching an acceptable number of defects, or when a certain amount of time has elapsed in computing each round of ply scenarios.” [Page 77 Par 1 – Page 78 Par 1] “The goal of the Computer Aided Processes Planning (CAPP) software is to implement and appropriately automate the process planning functions identified in the down selection process of Chapter 4 and further expanded upon in Chapter 5. The following chapter discusses the creation of the CAPP software and how it aids in the rapid prototyping design phase of composite laminates along with the ply level optimization to reduce geometry related fiber defects. The methods laid out will ultimately help find the ideal starting points and layup strategies to reduce the fiber defects. The CAPP software is broken down into three major functions. First, the software helps to create several ply scenarios by locating starting points with potential layup strategies and presents the resulting geometrical fiber defect instance and severity measurements. The second portion allows the process planner to define the relative importance of defect types in order to create an overall ranking of the defect set that is used for the ply level optimization. The final function presents final scores for each ply scenario and organizes them by starting point and the chosen layup strategy. These scores can be used to decide if a satisfactory solution has been reached or if additional iterations should be performed. Minimizing the ply’s overall score, or objective function, indicates that a solution has been reached which adequately minimizes the prioritized defect types. These functionalities are defined as the three “Majors” of the software and are described in sections 6.5, 6.6, and 6.7.” [Page 84 Par 5] “Each starting point will have a specific layup strategy associated with it. These layup strategies are created in VCP and use the principles set out in Chapter 2. The process planner will have the option of choosing which layup strategy they want each time they create a starting point array.” [Page 79 Par 1] “The flow diagram presents the CAPP’s relationship with the other software and which types of data are shared during each step to achieve the laminate optimization. The process begins with the CO, which passes the basic laminate definitions to the CAPP. These laminate definitions contain the tool surface on which the laminate will be constructed as well as the ply boundary and ply angles that define each individual ply in that laminate. The CAPP takes this laminate definition and constructs and enters the ply level optimization loop with VCP. Here, different laminates are iterated upon to minimize the prioritized defects. VCP enables this iteration by building the geometry of the ply scenarios supplied by the CAPP and identifying the resulting geometric defects. After a sufficiently optimized laminate has been generated, the process planning results can be used to continue the laminate design phase.” [Page 90 Par 2 – Page 91 Par 2] “Major 1 has generated a normalized set of instance and severity measurements for each feature of interest. Major 2 provides a method for creating an overall ranking of many features with a series of pair-wise comparisons through the Analytical Hierarchy process. Major 3 combines the data into a single normalized score for each ply scenario. The lowest score will identify the best starting point and layup strategy of that iteration according to the thresholds and preferences used. Creating the array of starting points earlier will help the process planner as a variety of potential starting points can be easily compared to each other for each ply. The display for Major 3 can be seen in Figure 6.18. The intuitive display clearly shows which starting points are the best by color coding them on a scale from red, being worst, to green, being best. When creating the next iteration of starting points, the CAPP module will select the starting point and strategy with the lowest score. Through multiple iterations the best scenario can be located with minimal effort.” [Page 50 Par 3] “A stagger shift (Figure 4.6) is when a process planner staggers two similarly oriented plies by shifting the top ply with respect to the bottom ply. A stagger shift is usually used when gaps or overlaps are present in a ply so that these defects do not propagate to a subsequent ply oriented in the same direction.” [Figure 6.5a] Shows the interface for dealing with several stacked plies)
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Halbritter_2020 does not explicitly teach using surrogate modeling optimization to search a ply level design space
Noevere makes obvious using surrogate modeling optimization to search a ply level design space ([Page 6 Par 14] “The initial model presented in Ref. [3] performs simulations on flat tooling. In this mode of operation, the model will be used to generate a surrogate model (via polynomial regression) that estimates the probability of AFP defects as a function of steering radius and other process parameters.” [Page 10 Par 2-5] “Surrogate models are often used to represent time-consuming higher fidelity analyses (also known as response surface models). Polynomial regression was used to generate the surrogate model for the flat surface AFP defect simulation. … The exponents 𝐸𝑖 are defined by the selected form of the polynomial. The coefficients 𝐶𝑖 are generated when regression is performed against a sensitivity analysis of AFP process parameters. After the sensitivity analysis of process parameters has been run through the AFP defects simulation described previously, the probability of AFP defects is extracted from the simulation results. These datapoints are used in least squares regression to produce the coefficients described above. … The surrogate model is utilized in the Central Optimizer process to evaluate steering radii predicted by VCP. VCP can export steering radii for each ply across the entirety of the tool surface. Each radius is evaluated with the surrogate model to produce a probability map of defect likelihood across the entire tool surface, for each ply.”)
Noevere is analogous art because it is within the field of automated fiber placement optimization. It would have been obvious to one of ordinary skill in the art to combine Noevere with Halbritter_2020 in order to more efficiently perform analysis of the various generated configurations. Halbritter_2020 notes the excessive processing time required for analyzing an individual ply using conventional methods ([Page 75 Par 1] “… Due to the time required to compute the ply from the specified inputs, it is not currently feasible to test every possible pair of inputs …”) To this end Noevere uses surrogate modelling to perform ply-level analysis, resulting in significantly more efficient modelling, noting how surrogate models are a common method for efficiently representing otherwise time-consuming analyses. ([Page 10 Par 2] “Surrogate models are often used to represent time-consuming higher fidelity analyses (also known as response surface models). Polynomial regression was used to generate the surrogate model for the flat surface AFP defect simulation.”) Overall, one of ordinary skill in the art would have recognized that combining Halbritter_2020 with Noevere would result in a significantly more efficient modeling system, allowing optimized designs to be achieved more quickly and efficiently.
Claim 17. Halbritter_2020 teaches wherein said one or more processors are further programmed for generating a reference curve for use in ply level design of the planned laminate. ([Page 7 Par 3] “In order to propagate fiber paths across the tool surface, an initial (or reference) curve is needed. In this section, the different strategies to determine the reference curve will be detailed.” [Page 62 Par 2] “The inputs will be two of the functions defined in Chapter 4, the starting point (section 4.1.2) and the chosen layup strategy (section 4.1.4). These are two of the functions that were identified from the process planning survey as benefiting from some automation, additionally these have a very direct effect on the resulting ply. The outputs are the defects directly resulting from the geometrical relationship between the tool surface and the created plies. The defects chosen here were gaps and overlaps (section 3.2.1), angle deviation (section 3.2.6), and fiber steering. Fiber steering can be directly measured from the geometry of the created fiber paths” [Page 63 Par 3] “The measurement of gaps and overlaps requires the parametric tool surface and the generated layup paths to be investigated.” [Figure 5.7a] Shows generated fiber paths across the surface)
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Claim 18. Halbritter_2020 teaches wherein the reference curve is generated through use of one of fixed angle, geodesic, and variable angle methodologies. ([Page 7 Par 3 – Page 11 Par 3] “In order to propagate fiber paths across the tool surface, an initial (or reference) curve is needed. In this section, the different strategies to determine the reference curve will be detailed. … 2.1.5 Geodesic Guide Curves The geodesic path can also be known as the natural path. A geodesic is the shortest path between two points along a three-dimensional surface in Cartesian space [22]. This is why the geodesic path is a straight line on a flat panel [20]. Also, a geodesic path can be obtained by specifying a starting point and a direction of travel.” [Page 62 Par 2] “The inputs will be two of the functions defined in Chapter 4, the starting point (section 4.1.2) and the chosen layup strategy (section 4.1.4). These are two of the functions that were identified from the process planning survey as benefiting from some automation, additionally these have a very direct effect on the resulting ply. The outputs are the defects directly resulting from the geometrical relationship between the tool surface and the created plies. The defects chosen here were gaps and overlaps (section 3.2.1), angle deviation (section 3.2.6), and fiber steering. Fiber steering can be directly measured from the geometry of the created fiber paths” [Page 63 Par 3] “The measurement of gaps and overlaps requires the parametric tool surface and the generated layup paths to be investigated.” [Figure 5.7a] Shows generated fiber paths across the surface)
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Claim 19. Halbritter_2020 teaches wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) in connection with VERICUT Composite Programming (VCP). ([Page 82 Par 2 – Page 83 Par 1] “6.3 CREATION OF PLY SCENARIOS The CAPP’s initial function is to find the starting points for different tools and boundaries and then export these to the Vericut Composites Program (VCP) software. As regularly stated in section 4.1.2, finding the correct starting point is a critical function of process planning. Finding the correct starting point can be a tedious and difficult task that benefits greatly from automation. To help reduce the time for this task the CAPP module finds an array of starting points for each ply.”)
Claim 21. Halbritter_2020 teaches wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for further optimizing the interaction of defects between individual plies through the thickness of the laminate. ([Page 77 Par 1] “The goal of the Computer Aided Processes Planning (CAPP) software is to implement and appropriately automate the process planning functions identified in the down selection process of Chapter 4 and further expanded upon in Chapter 5. The following chapter discusses the creation of the CAPP software and how it aids in the rapid prototyping design phase of composite laminates along with the ply level optimization to reduce geometry related fiber defects. The methods laid out will ultimately help find the ideal starting points and layup strategies to reduce the fiber defects.” [Page 62 Par 1-2] “PLY LEVEL OPTIMIZATION The creation of the best layups can be approached as an optimization problem. In order to perform this optimization, it is necessary to quantify and score the layups generated with the strategies defined in Chapter 2. For this approach, a ply level optimization is performed, where each ply is individually tailored in order to minimize its objective function. The objective function allows us to quantify the resulting plies according to the following sets of inputs and outputs. The inputs will be two of the functions defined in Chapter 4, the starting point (section 4.1.2) and the chosen layup strategy (section 4.1.4). These are two of the functions that were identified from the process planning survey as benefiting from some automation, additionally these have a very direct effect on the resulting ply. The outputs are the defects directly resulting from the geometrical relationship between the tool surface and the created plies. The defects chosen here were gaps and overlaps (section 3.2.1), angle deviation (section 3.2.6), and fiber steering. Fiber steering can be directly measured from the geometry of the created fiber paths and can lead to several other defects: puckers (section 3.2.2), wrinkles (3.2.3), and folds (3.2.7)… This chapter will define the techniques used for measuring the fiber defects and how they are used to create the objective function. Following this, the iterative method used to approach an optimized ply using the objective function is described.” [Page 68 Par 3 – Page 69 Par 3] “Process planning works to minimize defects and unwanted features that will develop based on the chosen process planning parameters. … To summarize these features, two aspects of their presence must be considered. The majority of manufacturing and design processes set a maximum instance threshold for each feature. An instance threshold measures total amount of defects above a certain threshold. These limitations are imposed to reduce the propagation of more defects and to improve the manufacturability of the laminate. The other measurement used by process planners is severity. The severity threshold of the defect finds the amount that the instances above the threshold value affect the total amount of defects. This measures how significantly certain defects will affect the overall laminate and will demonstrate if there are many small defects or several large defects. The Instance functions as an indicator of commonality of the features for the given ply. The Severity of the feature uses the threshold to find the sum of the instances as they occur in the ply.” [Page 33 Par 1-2] “A gap occurs when two adjacent tows are not perfectly laid up adjacent to each other resulting in a gap between the tows. An overlap is when the two adjacent tows are overlapping onto each other. The most common cause of gaps and overlaps is steering during layup since, the tows in a course will not fit together perfectly, especially when adopting a parallel coverage strategy. However, gaps and overlaps can naturally occur outside of steering if laying up over a complex 3D tool surface. Gaps and overlaps are well anticipated in areas of purposeful steering, or on doubly curved surfaces where adequate roller compaction is not guaranteed. From the geometry of these situations, the gaps and overlaps can be directly computed. The measurement of gaps and overlaps are generally easy to detect due to the build-up or lack of material along the course. As successive plies are laid up over overlaps, consistently at the same location at each layer, significant thickness buildup will be visible. A gap and/or overlap may become a site for failure initiation under loads. Gaps would create resin rich regions for crack growth while overlaps create undulation in the fiber that can lead to compressive failures. They may also become a site for wrinkling in the layers placed over them in the succeeding layer.” [Examiner’s note: minimizing gaps and overlaps optimizes the interactions between individual plies through the thickness of the laminate])
Claim 22. Halbritter_2020 teaches wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for beginning with a laminate skeleton which defines ply boundaries and associated fiber angles. ([Page 79 Par 1] “The flow diagram presents the CAPP’s relationship with the other software and which types of data are shared during each step to achieve the laminate optimization. The process begins with the CO, which passes the basic laminate definitions to the CAPP. These laminate definitions contain the tool surface on which the laminate will be constructed as well as the ply boundary and ply angles that define each individual ply in that laminate.” [Page 75 Par 1] “Each ply is initially defined by a ply boundary and ply angle. These provide the basis for the defined plies of the laminate;”)
Claim 23. Halbritter_2020 teaches wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for planning a laminate scenario which comprises a collection of ply scenarios which respectively carry over starting point location, path geometry, and the subsequent predicted defect geometry. ([Page 79 Par 1 – Page 85 Par 2] “The flow diagram presents the CAPP’s relationship with the other software and which types of data are shared during each step to achieve the laminate optimization. The process begins with the CO, which passes the basic laminate definitions to the CAPP. These laminate definitions contain the tool surface on which the laminate will be constructed as well as the ply boundary and ply angles that define each individual ply in that laminate. The CAPP takes this laminate definition and constructs and enters the ply level optimization loop with VCP. Here, different laminates are iterated upon to minimize the prioritized defects. VCP enables this iteration by building the geometry of the ply scenarios supplied by the CAPP and identifying the resulting geometric defects. After a sufficiently optimized laminate has been generated, the process planning results can be used to continue the laminate design phase. … 6.2 SOFTWARE INTERFACE OVERVIEW The CAPP software is a standalone package that can opened to the following interface presented in Figure 6.2. The individual portions of the software are presented and described in the following sections in the order that typical user would interact with them. 6.2.1 Laminate Tree As stated previously, the CAPP module begins by opening a laminate definition and a surface path that have been defined by the CO. The interface looks like the one presented in Figure 6.3a. The structure of the laminate can be observed in Figure 6.3b, where each ply, its ply angle, boundary name, and its various ply scenarios are organized. 6.2.2 Viewer Options Once the analysis is complete and the VCP results are reimported into the CAPP module then many of the viewer options can be used. These options help visualize the tool and the defects giving the process planner a better understanding of the manufacturability of the part. The viewer options are shown in Figure 6.4a-b along with the tooltip of their functionality. The viewing options, especially the defect toolbar, work in close tandem with options in Major 1, and can only be used when the specific types of data have been analyzed and imported to the CAPP software. 6.2.3 Operations and Batch Analysis Interfaces The initial state for optimization is created through the interface featured in Figure 6.5. This set of functionality performs the surface splitting, HKS computation, and surface center point discovery for each ply contained in the laminate. In the same toolbar, there is also the option to create the next iteration and refresh the viewer. This process can also be done individually by right-clicking any of the plies. … 6.3 CREATION OF PLY SCENARIOS The CAPP’s initial function is to find the starting points for different tools and boundaries and then export these to the Vericut Composites Program (VCP) software. As regularly stated in section 4.1.2, finding the correct starting point is a critical function of process planning. Finding the correct starting point can be a tedious and difficult task that benefits greatly from automation. To help reduce the time for this task the CAPP module finds an array of starting points for each ply. The following section discusses how the starting points are located. The CAPP module takes in a Catia Laminate consisting of a laminate definition and surface path. This sets up the ply boundaries and tool surface as seen below in Figure 6.7. The starting points can now be identified for all the plies that create the laminate. The CAPP module performs the following three steps in order to identify the starting points: Splits the surface at the ply boundary so only the area inside the surface is worked on; finds the Heat Kernel Signature (HKS) on the area inside the ply boundary; create an array of starting points where the HKS is the “hottest”. The process is relatively simple but greatly reduces the time it takes for process planners to find starting points. With three clicks, the starting points for all the plies can be generated. The following contains a detailed explanation of each of these steps. In step 1, the software locates the ply boundary and isolates the surface inside as in Figure 6.8a. This is important so that HKS calculations occur in the correct area and the starting points for the specific ply being worked on are found. In step 2, the software performs an HKS calculation on the part. This essentially heats up the part and sees where the heat is last to dissipate from. The results of an HKS calculation can be seen in Figure 6.8b. The final step creates an array of starting points at the “hottest” part of the tool as indicated by the HKS results. An array of 9 potential starting points is placed on the part. Having an array of starting points will help create iterations later to find the best possible starting point. Each starting point will have a specific layup strategy associated with it. These layup strategies are created in VCP and use the principles set out in Chapter 2. The process planner will have the option of choosing which layup strategy they want each time they create a starting point array. … 6.4 BATCH PROCESSING OF PLY SCENARIOS Once the ply scenarios have been initialized, the CAPP module can create a VCP batch file which contains their ply definitions. This batch file is imported into VCP where the specified starting points are investigated with their associated layup strategies. VCP generates the plies from these ply scenario definitions and finds the defects present. The gaps, overlaps, angle deviation, and steering angle defect results are included in a report that is imported back into the CAPP module.” [Page 91 Par 2] “The intuitive display clearly shows which starting points are the best by color coding them on a scale from red, being worst, to green, being best. When creating the next iteration of starting points, the CAPP module will select the starting point and strategy with the lowest score. Through multiple iterations the best scenario can be located with minimal effort.”)
Claim 26. Halbritter_2020 teaches wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for considering the defects ([Figure 6.4] Shows the interface for visibility control within the CAPP software. Note the “Show Defects” button)
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Noevere makes obvious considering defects for all plies globally and projecting them onto a common surface. ([Figure 2] Shows the CAPP software with a particular laminate using multiple plies. Note the that “show defects” icon near the bottom left of the viewport is selected; the green and red lines on the displayed surface represent defects)
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Claim 27. Halbritter_2020 teaches wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for predicting tow gaps, overlaps, angle deviations, and steering performed with the VERICUT Composite Programming (VCP) tool. ([Page 62 Par 1-2] “PLY LEVEL OPTIMIZATION The creation of the best layups can be approached as an optimization problem. In order to perform this optimization, it is necessary to quantify and score the layups generated with the strategies defined in Chapter 2. For this approach, a ply level optimization is performed, where each ply is individually tailored in order to minimize its objective function. The objective function allows us to quantify the resulting plies according to the following sets of inputs and outputs. The inputs will be two of the functions defined in Chapter 4, the starting point (section 4.1.2) and the chosen layup strategy (section 4.1.4). These are two of the functions that were identified from the process planning survey as benefiting from some automation, additionally these have a very direct effect on the resulting ply. The outputs are the defects directly resulting from the geometrical relationship between the tool surface and the created plies. The defects chosen here were gaps and overlaps (section 3.2.1), angle deviation (section 3.2.6), and fiber steering. Fiber steering can be directly measured from the geometry of the created fiber paths and can lead to several other defects: puckers (section 3.2.2), wrinkles (3.2.3), and folds (3.2.7)… This chapter will define the techniques used for measuring the fiber defects and how they are used to create the objective function. Following this, the iterative method used to approach an optimized ply using the objective function is described.” [Page 79 Par 1] “The CAPP takes this laminate definition and constructs and enters the ply level optimization loop with VCP. Here, different laminates are iterated upon to minimize the prioritized defects. VCP enables this iteration by building the geometry of the ply scenarios supplied by the CAPP and identifying the resulting geometric defects.”)
Claim 30. Halbritter_2020 teaches wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for identifying stacked defects throughout the entire thickness of the laminate. ([Abstract] “Process planning represents an essential stage of the Automated Fiber Placement (AFP) workflow. It develops useful and efficient machine processes based upon the working material, composite design, and manufacturing resources. …This document presents an automated software solution to the layup strategy and starting point selection phase of Process Planning. A series of ply scenarios are generated with variations of these ply parameters and evaluated according to a set of metrics entered by the Process Planner. These metrics are generated through use of the Analytical Hierarchy Process (AHP), where relative importance between each of the fiber features are defined. The ply scenarios are selected which reduce the overall fiber feature scores based on the defects the Process Planner wishes to minimize.” [Page 77 Par 1] “The goal of the Computer Aided Processes Planning (CAPP) software is to implement and appropriately automate the process planning functions identified in the down selection process of Chapter 4 and further expanded upon in Chapter 5. The following chapter discusses the creation of the CAPP software and how it aids in the rapid prototyping design phase of composite laminates along with the ply level optimization to reduce geometry related fiber defects. The methods laid out will ultimately help find the ideal starting points and layup strategies to reduce the fiber defects.” [Page 62 Par 1-2] “PLY LEVEL OPTIMIZATION The creation of the best layups can be approached as an optimization problem. In order to perform this optimization, it is necessary to quantify and score the layups generated with the strategies defined in Chapter 2. For this approach, a ply level optimization is performed, where each ply is individually tailored in order to minimize its objective function. The objective function allows us to quantify the resulting plies according to the following sets of inputs and outputs. The inputs will be two of the functions defined in Chapter 4, the starting point (section 4.1.2) and the chosen layup strategy (section 4.1.4). These are two of the functions that were identified from the process planning survey as benefiting from some automation, additionally these have a very direct effect on the resulting ply. The outputs are the defects directly resulting from the geometrical relationship between the tool surface and the created plies. The defects chosen here were gaps and overlaps (section 3.2.1), angle deviation (section 3.2.6), and fiber steering. Fiber steering can be directly measured from the geometry of the created fiber paths and can lead to several other defects: puckers (section 3.2.2), wrinkles (3.2.3), and folds (3.2.7)… This chapter will define the techniques used for measuring the fiber defects and how they are used to create the objective function. Following this, the iterative method used to approach an optimized ply using the objective function is described.” [Page 68 Par 3 – Page 69 Par 3] “Process planning works to minimize defects and unwanted features that will develop based on the chosen process planning parameters. … To summarize these features, two aspects of their presence must be considered. The majority of manufacturing and design processes set a maximum instance threshold for each feature. An instance threshold measures total amount of defects above a certain threshold. These limitations are imposed to reduce the propagation of more defects and to improve the manufacturability of the laminate. The other measurement used by process planners is severity. The severity threshold of the defect finds the amount that the instances above the threshold value affect the total amount of defects. This measures how significantly certain defects will affect the overall laminate and will demonstrate if there are many small defects or several large defects. The Instance functions as an indicator of commonality of the features for the given ply. The Severity of the feature uses the threshold to find the sum of the instances as they occur in the ply.” [Page 33 Par 1-2] “A gap occurs when two adjacent tows are not perfectly laid up adjacent to each other resulting in a gap between the tows. An overlap is when the two adjacent tows are overlapping onto each other. The most common cause of gaps and overlaps is steering during layup since, the tows in a course will not fit together perfectly, especially when adopting a parallel coverage strategy. However, gaps and overlaps can naturally occur outside of steering if laying up over a complex 3D tool surface. Gaps and overlaps are well anticipated in areas of purposeful steering, or on doubly curved surfaces where adequate roller compaction is not guaranteed. From the geometry of these situations, the gaps and overlaps can be directly computed. The measurement of gaps and overlaps are generally easy to detect due to the build-up or lack of material along the course. As successive plies are laid up over overlaps, consistently at the same location at each layer, significant thickness buildup will be visible. A gap and/or overlap may become a site for failure initiation under loads. Gaps would create resin rich regions for crack growth while overlaps create undulation in the fiber that can lead to compressive failures. They may also become a site for wrinkling in the layers placed over them in the succeeding layer.” [Page 79 Par 1] “The flow diagram presents the CAPP’s relationship with the other software and which types of data are shared during each step to achieve the laminate optimization. The process begins with the CO, which passes the basic laminate definitions to the CAPP. These laminate definitions contain the tool surface on which the laminate will be constructed as well as the ply boundary and ply angles that define each individual ply in that laminate. The CAPP takes this laminate definition and constructs and enters the ply level optimization loop with VCP. Here, different laminates are iterated upon to minimize the prioritized defects. VCP enables this iteration by building the geometry of the ply scenarios supplied by the CAPP and identifying the resulting geometric defects. After a sufficiently optimized laminate has been generated, the process planning results can be used to continue the laminate design phase.”)
Claim 32. Halbritter_2020 teaches wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for measuring and scoring stacked defects relying on determined defect levels. ([Abstract] “Process planning represents an essential stage of the Automated Fiber Placement (AFP) workflow. It develops useful and efficient machine processes based upon the working material, composite design, and manufacturing resources. …This document presents an automated software solution to the layup strategy and starting point selection phase of Process Planning. A series of ply scenarios are generated with variations of these ply parameters and evaluated according to a set of metrics entered by the Process Planner. These metrics are generated through use of the Analytical Hierarchy Process (AHP), where relative importance between each of the fiber features are defined. The ply scenarios are selected which reduce the overall fiber feature scores based on the defects the Process Planner wishes to minimize.” [Page 77 Par 1] “The goal of the Computer Aided Processes Planning (CAPP) software is to implement and appropriately automate the process planning functions identified in the down selection process of Chapter 4 and further expanded upon in Chapter 5. The following chapter discusses the creation of the CAPP software and how it aids in the rapid prototyping design phase of composite laminates along with the ply level optimization to reduce geometry related fiber defects. The methods laid out will ultimately help find the ideal starting points and layup strategies to reduce the fiber defects.” [Page 62 Par 1-2] “PLY LEVEL OPTIMIZATION The creation of the best layups can be approached as an optimization problem. In order to perform this optimization, it is necessary to quantify and score the layups generated with the strategies defined in Chapter 2. For this approach, a ply level optimization is performed, where each ply is individually tailored in order to minimize its objective function. The objective function allows us to quantify the resulting plies according to the following sets of inputs and outputs. The inputs will be two of the functions defined in Chapter 4, the starting point (section 4.1.2) and the chosen layup strategy (section 4.1.4). These are two of the functions that were identified from the process planning survey as benefiting from some automation, additionally these have a very direct effect on the resulting ply. The outputs are the defects directly resulting from the geometrical relationship between the tool surface and the created plies. The defects chosen here were gaps and overlaps (section 3.2.1), angle deviation (section 3.2.6), and fiber steering. Fiber steering can be directly measured from the geometry of the created fiber paths and can lead to several other defects: puckers (section 3.2.2), wrinkles (3.2.3), and folds (3.2.7)… This chapter will define the techniques used for measuring the fiber defects and how they are used to create the objective function. Following this, the iterative method used to approach an optimized ply using the objective function is described.” [Page 68 Par 3 – Page 69 Par 3] “Process planning works to minimize defects and unwanted features that will develop based on the chosen process planning parameters. … To summarize these features, two aspects of their presence must be considered. The majority of manufacturing and design processes set a maximum instance threshold for each feature. An instance threshold measures total amount of defects above a certain threshold. These limitations are imposed to reduce the propagation of more defects and to improve the manufacturability of the laminate. The other measurement used by process planners is severity. The severity threshold of the defect finds the amount that the instances above the threshold value affect the total amount of defects. This measures how significantly certain defects will affect the overall laminate and will demonstrate if there are many small defects or several large defects. The Instance functions as an indicator of commonality of the features for the given ply. The Severity of the feature uses the threshold to find the sum of the instances as they occur in the ply.” [Page 33 Par 1-2] “A gap occurs when two adjacent tows are not perfectly laid up adjacent to each other resulting in a gap between the tows. An overlap is when the two adjacent tows are overlapping onto each other. The most common cause of gaps and overlaps is steering during layup since, the tows in a course will not fit together perfectly, especially when adopting a parallel coverage strategy. However, gaps and overlaps can naturally occur outside of steering if laying up over a complex 3D tool surface. Gaps and overlaps are well anticipated in areas of purposeful steering, or on doubly curved surfaces where adequate roller compaction is not guaranteed. From the geometry of these situations, the gaps and overlaps can be directly computed. The measurement of gaps and overlaps are generally easy to detect due to the build-up or lack of material along the course. As successive plies are laid up over overlaps, consistently at the same location at each layer, significant thickness buildup will be visible. A gap and/or overlap may become a site for failure initiation under loads. Gaps would create resin rich regions for crack growth while overlaps create undulation in the fiber that can lead to compressive failures. They may also become a site for wrinkling in the layers placed over them in the succeeding layer.” [Page 79 Par 1] “The flow diagram presents the CAPP’s relationship with the other software and which types of data are shared during each step to achieve the laminate optimization. The process begins with the CO, which passes the basic laminate definitions to the CAPP. These laminate definitions contain the tool surface on which the laminate will be constructed as well as the ply boundary and ply angles that define each individual ply in that laminate. The CAPP takes this laminate definition and constructs and enters the ply level optimization loop with VCP. Here, different laminates are iterated upon to minimize the prioritized defects. VCP enables this iteration by building the geometry of the ply scenarios supplied by the CAPP and identifying the resulting geometric defects. After a sufficiently optimized laminate has been generated, the process planning results can be used to continue the laminate design phase.”)
(2) Claims 4 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Automation of Process Planning for Automated Fiber Placement (Hereinafter Halbritter_2020) in view of Development of a Design for Manufacturing Tool for Automated Fiber Placement Structure (Hereinafter Noevere) in further view of The impact of gaps and overlaps on variable stiffness composites manufactured by Automated Fiber Placement (Hereinafter Fayazbakhsh)
Claim 4. Noevere teaches wherein the surrogate-based methods comprise surrogate-based optimization (SBO) following the steps of: (1) Initial sample selection, (2) Construct surrogate model based on training data, ([Page 10 Par 2-4] “Surrogate models are often used to represent time-consuming higher fidelity analyses (also known as response surface models). Polynomial regression was used to generate the surrogate model for the flat surface AFP defect simulation. The form of the regression is shown in the equations below. Equation (1) is the regression polynomial whose form is chosen by the user. Equation (2) is a logistic function that has been adapted to produce defect probabilities varying from 0 to 100%.”) The exponents 𝐸𝑖 are defined by the selected form of the polynomial. The coefficients 𝐶𝑖 are generated when regression is performed against a sensitivity analysis of AFP process parameters. After the sensitivity analysis of process parameters has been run through the AFP defects simulation described previously, the probability of AFP defects is extracted from the simulation results. These datapoints are used in least squares regression to produce the coefficients described above. An example of the regression is shown in Figure 10.”)
The combination of Halbritter_2020 and Noevere does not explicitly teach (2) Construct surrogate model based on training data, (3) Search surrogate model for next location of interest,
(4) Run analysis and update model at the new locations found by search, and
(5) Repeat steps (2) through (4) until a convergence criterion is met.
Fayazbakhsh teaches (2) Construct surrogate model based on training data, (3) Search surrogate model for next location of interest, (4) Run analysis and update model at the new locations found by search, and (5) Repeat steps (2) through (4) until a convergence criterion is met. ([Page 20 Par 3] “Non-dominated sorting genetic algorithm-II (NSGA-II) is a multi-objective Evolutionary Algorithm (EA) that uses elite-preservation strategy and explicit diversity-preserving mechanism to find a set of evenly distributed solutions to a multi-objective optimization problem [58].” [Page 22 Par 3 – Page 23 Par 4] “The main steps implemented here for a surrogate-based NSGA-II are (Figure 2.3): Step 1- The initial sample points are generated by the Latin Hypercube (LH) method [61, 64]. The LH method is used to generate an initial population that is predominantly random, but is uniform in each dimension. Step 2- Two different surrogate models are constructed based on the initial sample points: the first one predicts the equivalent plate stiffness, and the second one predicts the corresponding buckling load. Step 3- The intermediate population (offspring) is generated by the standard genetic operators. Step 4- The individuals in the offspring are divided into two groups (based on their Euclidean distance to the parent individuals): those which lie in the local vicinity of the individuals of the parent population are evaluated by the surrogate model; the remaining individuals (second group) are assessed through true function evaluation. Step 5- The non-dominate sorting algorithm is applied to create a new population. Step 6- If any individual in the new population has been evaluated by the surrogate model, then it must be re-evaluated by the true functions before proceeding to the next step. Step 7- Proceed to step 2 unless the stopping criteria (which requires reach the maximum number of true function evaluation) is satisfied.”)
Fayazbakhsh is analogous art because it is within the field of Automated Fiber Placement optimization using surrogate modelling. It would have been obvious to one of ordinary skill in the art to combine Fayazbakhsh with Halbritter_2020 and Noevere in order to more efficiently find solutions. Fayazbakhsh notes how coupling genetic algorithms with surrogate modeling has shown to significantly speed up optimization processes ([Page 7 Par 1] “GA, however, being a population-based algorithm, requires a large number of function evaluations to reach the optimum solution [43]. This requirement makes the process of finding an optimum solution computationally expensive. To overcome this issue, the coupling of a surrogate model with GA has been recognized as beneficial.”) It should be noted that Halbritter_2020 uses genetic algorithms for some of its optimization ([Page 22 Par 3] “The fiber shape is defined in the polynomial functions while, using spline functions, simultaneous equations need to be solved for the same task. The path is then optimized by the genetic algorithm defined in the following section.”) To this end, Fayazbakhsh introduces an architecture for coupling a genetic algorithm with surrogate modelling to more efficiently optimize Automated Fiber Placement configurations, noting how the NSGA-II algorithm performs more efficiently than comparable optimization algorithms ([Page 20 Par 2 – Page 21 Par 1] “These values will be numerically calculated using finite element analysis of the plates, as explained in Section 2.3. It is worth to mention that the design variables should be integers to respect the manufacturing accuracy of AFP. To reduce the computational cost involved in the optimization process, we now resort to a surrogate modeling approach, which will be coupled with the optimization algorithm. 2.4.1. NSGA-II and motivation for surrogate modeling Non-dominated sorting genetic algorithm-II (NSGA-II) is a multi-objective Evolutionary Algorithm (EA) that uses elite-preservation strategy and explicit diversity-preserving mechanism to find a set of evenly distributed solutions to a multi-objective optimization problem [58]. NSGA-II was developed to reduce the computational complexity, to improve the diversity among non-dominated solutions, and to add elitism to non-dominated sorting genetic algorithm (NSGA) [59]. Simplicity, effectiveness, and independency on user-defined parameters, make NSGA-II a flexible and robust EA procedure for solving various multi objective optimization problems using a common framework [60]. As mentioned in the introduction, NSGA-II needs a large number of function evaluations to find the Pareto optimal set. For the case of this study, since the objective functions are evaluated through expensive simulations, the high computational cost of the multi-objective optimization becomes a major challenge. For this reason, a surrogate model is chosen to approximate the computationally expensive simulation with a multi-dimensional parametric surface [61, 62]. The surrogate model, which is significantly cheaper to evaluate than the high fidelity simulation, replaces the expensive simulations during the optimization process; as a result, the computational cost of the optimization process can be significantly reduced.”) Overall, one of ordinary skill in the art would have recognized that combining Fayazbakhsh with Halbritter_2020 and Noevere would result in an even more computationally efficient optimization process.
Claim 6. Noevere teaches wherein the surrogate-based methods([Page 10 Par 2-4] “Surrogate models are often used to represent time-consuming higher fidelity analyses (also known as response surface models). Polynomial regression was used to generate the surrogate model for the flat surface AFP defect simulation. The form of the regression is shown in the equations below. Equation (1) is the regression polynomial whose form is chosen by the user. Equation (2) is a logistic function that has been adapted to produce defect probabilities varying from 0 to 100%.”) The exponents 𝐸𝑖 are defined by the selected form of the polynomial. The coefficients 𝐶𝑖 are generated when regression is performed against a sensitivity analysis of AFP process parameters. After the sensitivity analysis of process parameters has been run through the AFP defects simulation described previously, the probability of AFP defects is extracted from the simulation results. These datapoints are used in least squares regression to produce the coefficients described above. An example of the regression is shown in Figure 10.”)
The combination of Halbritter_2020 and Noevere does not explicitly teach the surrogate-based methods include use of a gradient free method including use of one of (genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA).
Fayazbakhsh makes obvious the surrogate-based methods include use of a gradient free method including use of one of (genetic algorithm (GA), ([Page 20 Par 2 – Page 21 Par 1] “Where x is the vector of design variables, i.e., T0 and T1 are the fiber angles at the plate center and plate edges … These values will be numerically calculated using finite element analysis of the plates, as explained in Section 2.3. It is worth to mention that the design variables should be integers to respect the manufacturing accuracy of AFP. To reduce the computational cost involved in the optimization process, we now resort to a surrogate modeling approach, which will be coupled with the optimization algorithm. 2.4.1. NSGA-II and motivation for surrogate modeling Non-dominated sorting genetic algorithm-II (NSGA-II) is a multi-objective Evolutionary Algorithm (EA) that uses elite-preservation strategy and explicit diversity-preserving mechanism to find a set of evenly distributed solutions to a multi-objective optimization problem [58]. NSGA-II was developed to reduce the computational complexity, to improve the diversity among non-dominated solutions, and to add elitism to non-dominated sorting genetic algorithm (NSGA) [59]. Simplicity, effectiveness, and independency on user-defined parameters, make NSGA-II a flexible and robust EA procedure for solving various multi objective optimization problems using a common framework [60]. As mentioned in the introduction, NSGA-II needs a large number of function evaluations to find the Pareto optimal set. For the case of this study, since the objective functions are evaluated through expensive simulations, the high computational cost of the multi-objective optimization becomes a major challenge. For this reason, a surrogate model is chosen to approximate the computationally expensive simulation with a multi-dimensional parametric surface [61, 62]. The surrogate model, which is significantly cheaper to evaluate than the high fidelity simulation, replaces the expensive simulations during the optimization process; as a result, the computational cost of the optimization process can be significantly reduced.” [Page 22 Par 3 – Page 23 Par 4] “The main steps implemented here for a surrogate-based NSGA-II are (Figure 2.3): Step 1- The initial sample points are generated by the Latin Hypercube (LH) method [61, 64]. The LH method is used to generate an initial population that is predominantly random, but is uniform in each dimension. Step 2- Two different surrogate models are constructed based on the initial sample points: the first one predicts the equivalent plate stiffness, and the second one predicts the corresponding buckling load. Step 3- The intermediate population (offspring) is generated by the standard genetic operators. Step 4- The individuals in the offspring are divided into two groups (based on their Euclidean distance to the parent individuals): those which lie in the local vicinity of the individuals of the parent population are evaluated by the surrogate model; the remaining individuals (second group) are assessed through true function evaluation. Step 5- The non-dominate sorting algorithm is applied to create a new population. Step 6- If any individual in the new population has been evaluated by the surrogate model, then it must be re-evaluated by the true functions before proceeding to the next step. Step 7- Proceed to step 2 unless the stopping criteria (which requires reach the maximum number of true function evaluation) is satisfied.”) particle swarm optimization (PSO), and simulated annealing (SA).
Fayazbakhsh is analogous art because it is within the field of Automated Fiber Placement optimization using surrogate modelling. It would have been obvious to one of ordinary skill in the art to combine Fayazbakhsh with Halbritter_2020 and Noevere in order to more efficiently find solutions. Fayazbakhsh notes how coupling genetic algorithms with surrogate modeling has shown to significantly speed up optimization processes ([Page 7 Par 1] “GA, however, being a population-based algorithm, requires a large number of function evaluations to reach the optimum solution [43]. This requirement makes the process of finding an optimum solution computationally expensive. To overcome this issue, the coupling of a surrogate model with GA has been recognized as beneficial.”) It should be noted that Halbritter_2020 uses genetic algorithms for some of its optimization ([Page 22 Par 3] “The fiber shape is defined in the polynomial functions while, using spline functions, simultaneous equations need to be solved for the same task. The path is then optimized by the genetic algorithm defined in the following section.”) To this end, Fayazbakhsh introduces an architecture for coupling a genetic algorithm with surrogate modelling to more efficiently optimize Automated Fiber Placement configurations, noting how the NSGA-II algorithm performs more efficiently than comparable optimization algorithms ([Page 20 Par 2 – Page 21 Par 1] “These values will be numerically calculated using finite element analysis of the plates, as explained in Section 2.3. It is worth to mention that the design variables should be integers to respect the manufacturing accuracy of AFP. To reduce the computational cost involved in the optimization process, we now resort to a surrogate modeling approach, which will be coupled with the optimization algorithm. 2.4.1. NSGA-II and motivation for surrogate modeling Non-dominated sorting genetic algorithm-II (NSGA-II) is a multi-objective Evolutionary Algorithm (EA) that uses elite-preservation strategy and explicit diversity-preserving mechanism to find a set of evenly distributed solutions to a multi-objective optimization problem [58]. NSGA-II was developed to reduce the computational complexity, to improve the diversity among non-dominated solutions, and to add elitism to non-dominated sorting genetic algorithm (NSGA) [59]. Simplicity, effectiveness, and independency on user-defined parameters, make NSGA-II a flexible and robust EA procedure for solving various multi objective optimization problems using a common framework [60]. As mentioned in the introduction, NSGA-II needs a large number of function evaluations to find the Pareto optimal set. For the case of this study, since the objective functions are evaluated through expensive simulations, the high computational cost of the multi-objective optimization becomes a major challenge. For this reason, a surrogate model is chosen to approximate the computationally expensive simulation with a multi-dimensional parametric surface [61, 62]. The surrogate model, which is significantly cheaper to evaluate than the high fidelity simulation, replaces the expensive simulations during the optimization process; as a result, the computational cost of the optimization process can be significantly reduced.”) Overall, one of ordinary skill in the art would have recognized that combining Fayazbakhsh with Halbritter_2020 and Noevere would result in an even more computationally efficient optimization process.
(3) Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Automation of Process Planning for Automated Fiber Placement (Hereinafter Halbritter_2020) in view of Development of a Design for Manufacturing Tool for Automated Fiber Placement Structure (Hereinafter Noevere) in further view of Data-Driven Optimization Based on Random Forest Surrogate (Hereinafter Zheng)
Claim 5. Noevere teaches wherein the surrogate-based methods include use of a regression model ([Page 10 Par 2] “Surrogate models are often used to represent time-consuming higher fidelity analyses (also known as response surface models). Polynomial regression was used to generate the surrogate model for the flat surface AFP defect simulation.”)
The combination of Halbritter_2020 and Noevere does not explicitly teach wherein the surrogate-based methods include use of a model including one of Gaussian Process (GP), Support Vector (SV), and Random Forest (RF).
Zheng makes obvious wherein the surrogate-based methods include use of a model including one of Gaussian Process (GP), Support Vector (SV), and Random Forest (RF). ([Abstract] “For the practical problem that there are no exact evaluation functions to evaluate candidate solutions, it can only be solved by data-driven method. To solve these problems, Random Forests (RF) is used as surrogates to approximate the evaluation functions and constraint functions of constrained combinatorial optimization problems. In addition, support vector machine (SVM) model are introduced to rectify the surrogate-assisted fitness evaluations. The proposed error correction strategy is tested by a series of benchmarks of MOPKs. The results show that the proposed error correction strategy is more effective when RF is used as the surrogates to solve multi-objective combinatorial optimization problems with constraints.” [Page 488 Col 1 Par 2] “Because the candidate solutions of combinatorial optimization are not continuous, and current studies show that RF as the surrogate model of discrete decision variables is superior to RBF, we chooses RF model to approximate the optimization objectives and constraint functions of combinatorial optimization.”)
Zheng is analogous art because it is within the field of optimization using surrogate modelling. It would have been obvious to one of ordinary skill in the art to combine Zheng with Halbritter_2020 and Noevere before the effective filing date. One of ordinary skill in the art would have been motivated to make this combination in order to make the surrogate optimization process more accurate. Zheng notes how traditional surrogate-based optimization methods can quickly accumulate errors if not dealt with appropriately ([Page 487 Col 2 Par 1] “For constrained multi-objective combinatorial optimization problem, each constraint corresponding to a surrogate model, and the accumulation of multiple model errors will result in many unfeasible solutions being judged as feasible solutions, and the sampling of unfeasible solutions to the training set to update the surrogate model will seriously affect the EAs optimization performance based on the surrogate model.”) To this end, Zheng presents a method for correcting errors in surrogate modelling processes ([Page 487 Col 2 Par 2] “So, the main contributions of this work is to introduce SVM model to rectify surrogates for the constraint functions, and we improved the constraint surrogate models' accuracy for predicting which candidate solutions are Infeasible or feasible in each generations.” [Page 488 Col 2 Par 3 – Page 489 Col 1 Par 1] “As shown in Fig.2, points A and B can be classified correctly after the constraint boundary is rectified, but point C still cannot be classified correctly. Therefore, we use SVM to estimate whether the solution in the optimization process will be classified incorrectly or correctly in the constraint surrogate model. … as the situation shown in Fig.3 we can see that in the figure, the black dots represent the incorrectly classified solutions of the constraint surrogate model, and the red dots represent the correctly classified solutions. SVM can classify the classification errors and correct solutions of constrained surrogate models nonlinearly by learning from the training set. If the solution a is classified as feasible on the constraint surrogate model, but is estimated to be a misclassified solution on the SVM, then the solution will be judged as infeasible in the end.”) Overall, one of ordinary skill in the art would have recognized that combining Zheng with Halbritter_2020 and Noevere would result in a more accurate optimization process.
(4) Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Automation of Process Planning for Automated Fiber Placement (Hereinafter Halbritter_2020) in view of Development of a Design for Manufacturing Tool for Automated Fiber Placement Structure (Hereinafter Noevere) in further view of Design of a Manufacturing Demonstration Unit for a Composite Lunar Landing Gear Strut (Hereinafter Mason)
Claim 20. Halbritter_2020 teaches wherein the planned laminate ([Abstract] “Process planning represents an essential stage of the Automated Fiber Placement (AFP) workflow. It develops useful and efficient machine processes based upon the working material, composite design, and manufacturing resources. …This document presents an automated software solution to the layup strategy and starting point selection phase of Process Planning. A series of ply scenarios are generated with variations of these ply parameters and evaluated according to a set of metrics entered by the Process Planner. These metrics are generated through use of the Analytical Hierarchy Process (AHP), where relative importance between each of the fiber features are defined. The ply scenarios are selected which reduce the overall fiber feature scores based on the defects the Process Planner wishes to minimize.” [Page 77 Par 1] “The goal of the Computer Aided Processes Planning (CAPP) software is to implement and appropriately automate the process planning functions identified in the down selection process of Chapter 4 and further expanded upon in Chapter 5. The following chapter discusses the creation of the CAPP software and how it aids in the rapid prototyping design phase of composite laminates along with the ply level optimization to reduce geometry related fiber defects. The methods laid out will ultimately help find the ideal starting points and layup strategies to reduce the fiber defects.” [Page 62 Par 1-2] “PLY LEVEL OPTIMIZATION The creation of the best layups can be approached as an optimization problem. In order to perform this optimization, it is necessary to quantify and score the layups generated with the strategies defined in Chapter 2. For this approach, a ply level optimization is performed, where each ply is individually tailored in order to minimize its objective function. The objective function allows us to quantify the resulting plies according to the following sets of inputs and outputs. The inputs will be two of the functions defined in Chapter 4, the starting point (section 4.1.2) and the chosen layup strategy (section 4.1.4). These are two of the functions that were identified from the process planning survey as benefiting from some automation, additionally these have a very direct effect on the resulting ply. The outputs are the defects directly resulting from the geometrical relationship between the tool surface and the created plies. The defects chosen here were gaps and overlaps (section 3.2.1), angle deviation (section 3.2.6), and fiber steering. Fiber steering can be directly measured from the geometry of the created fiber paths and can lead to several other defects: puckers (section 3.2.2), wrinkles (3.2.3), and folds (3.2.7)… This chapter will define the techniques used for measuring the fiber defects and how they are used to create the objective function. Following this, the iterative method used to approach an optimized ply using the objective function is described.” [Page 68 Par 3 – Page 69 Par 3] “Process planning works to minimize defects and unwanted features that will develop based on the chosen process planning parameters. … To summarize these features, two aspects of their presence must be considered. The majority of manufacturing and design processes set a maximum instance threshold for each feature. An instance threshold measures total amount of defects above a certain threshold. These limitations are imposed to reduce the propagation of more defects and to improve the manufacturability of the laminate. The other measurement used by process planners is severity. The severity threshold of the defect finds the amount that the instances above the threshold value affect the total amount of defects. This measures how significantly certain defects will affect the overall laminate and will demonstrate if there are many small defects or several large defects. The Instance functions as an indicator of commonality of the features for the given ply. The Severity of the feature uses the threshold to find the sum of the instances as they occur in the ply.” [Page 33 Par 1-2] “A gap occurs when two adjacent tows are not perfectly laid up adjacent to each other resulting in a gap between the tows. An overlap is when the two adjacent tows are overlapping onto each other. The most common cause of gaps and overlaps is steering during layup since, the tows in a course will not fit together perfectly, especially when adopting a parallel coverage strategy. However, gaps and overlaps can naturally occur outside of steering if laying up over a complex 3D tool surface. Gaps and overlaps are well anticipated in areas of purposeful steering, or on doubly curved surfaces where adequate roller compaction is not guaranteed. From the geometry of these situations, the gaps and overlaps can be directly computed. The measurement of gaps and overlaps are generally easy to detect due to the build-up or lack of material along the course. As successive plies are laid up over overlaps, consistently at the same location at each layer, significant thickness buildup will be visible. A gap and/or overlap may become a site for failure initiation under loads. Gaps would create resin rich regions for crack growth while overlaps create undulation in the fiber that can lead to compressive failures. They may also become a site for wrinkling in the layers placed over them in the succeeding layer.” [Page 79 Par 1] “The flow diagram presents the CAPP’s relationship with the other software and which types of data are shared during each step to achieve the laminate optimization. The process begins with the CO, which passes the basic laminate definitions to the CAPP. These laminate definitions contain the tool surface on which the laminate will be constructed as well as the ply boundary and ply angles that define each individual ply in that laminate. The CAPP takes this laminate definition and constructs and enters the ply level optimization loop with VCP. Here, different laminates are iterated upon to minimize the prioritized defects. VCP enables this iteration by building the geometry of the ply scenarios supplied by the CAPP and identifying the resulting geometric defects. After a sufficiently optimized laminate has been generated, the process planning results can be used to continue the laminate design phase.” [Page 92 Par 2] “The final score is found by multiplying the instance and severity percentages by the ranking percentages to find the overall score (Figure 4.21).”)
The combination of Halbritter_2020 and Noevere does not explicitly teach wherein the planned laminate comprises a strut; a strut geometry
Mason makes obvious wherein the planned laminate comprises a strut; a strut geometry ([Abstract] “The goal of the current work was to develop an analytical framework for design of composite struts using various levels of model fidelity. … An additional goal of this work was to perform an initial assessment of using automated fiber placement (AFP) and other advanced manufacturing methods to explore their feasibility for the fabrication of struts for lunar landers, strut-braced wings, and other aerospace components.” [Page 2 Par 4 – Page 3 Par 1] “The Northrop-Grumman-designed strut assembly consisted of a composite tubular body and two metallic fitting regions, as shown in Fig. 3. The composite body was a single-piece tubular laminate with tapered ends that attach to an end fitting region, as shown in Fig. 4a. Composite overwraps for increased hoop strength were added in the fitting region. The metallic inserts in the fitting region of the strut were made of 6Al-4V titanium alloy and consisted of an integral corrugated insert. The tube laminate was IM7-8552 carbon fiber/epoxy with a ply thickness of 0.0052 in. and a stacking sequence of [90/02/±45/03/90]s [15]. The overwraps were 90° plies of the same carbon fiber/epoxy. Elastic properties for the titanium and carbon fiber/epoxy are given in Table 1.” [Fig. 3] Shows strut geometry)
Mason is analogous art because it is within the field of manufacturing using automated fiber placement. It would have been obvious to one of ordinary skill in the art to combine it with Halbritter_2020 and Noevere before the effective filing date. One of ordinary skill in the art would have been motivated to make this combination in order to make designed structures more structurally efficient, i.e. to increase the ratio of load-carrying ability to mass in the structure. Mason notes the desire for such increases in the field and presents a method for generating such improved structures ([Page 1 Par 1-2] “A mission of NASA is the design and evaluation of future aircraft and spacecraft concepts. Part of this mission involves evaluating the efficiency of structural concepts for aircraft and spacecraft components with the goal of designing lightweight parts. Currently, NASA researchers are interested in structurally efficient truss structures for application to spacecraft and aircraft components such as solar array supports, lunar lander struts, strut-braced wing components, and aircraft spars for unmanned aerial vehicles. Structural efficiency herein is defined as the ratio of the load-carrying ability to the mass of a structure. Studies involving the structural efficiency of shells have been ongoing at NASA for decades and continue today [1 to 7]. Composite materials and nontraditional designs offer opportunities for reduced mass compared to conventional aluminum trusses with uniform circular cross-sections. An example of a structure that would have been dependent upon struts in a truss arrangement is the Altair lunar lander [8] as shown in Fig. 1. Composite struts were evaluated for use with the Altair lander [9 to 17], and structurally efficient truss members such as composite struts are still of interest to NASA for space applications.” [Page 4 Par 2-3] “Initial guidance for the dimensions of the MDU strut design was available from the reference strut [15] as shown in Table 2, but some alterations were made based on available material and new geometry requirements. Specifically, the tube laminate composite was changed from IM7-8552 to Toray T800S-3900-25K [18] with a stacking sequence of [±45/0/45/90/-45/02/45/90/-45/0]s and a nominal ply thickness of 0.0039 in. Also, the strut length for the new design is different from the Altair [15] design, as given in Table 2. … are introduced by placing the same amount of composite material over a slightly varying circumference of the insert. The laminate stacking sequence of the tube was kept the same, but the ply thicknesses were varied with the intent of using standard and thin plies in the final manufactured part to produce a laminate with a stiffness similar to the one selected by the optimization process. The dimensions of the MDU were then iterated using these variables as discussed in the following sections.” [Page 6 Par 1] “An important objective of the present activity is to develop a robust process for tailoring a composite strut design that is lightweight and able to survive operational loads. To accomplish this objective, an optimization framework has been developed using the general-purpose optimization software LS-OPT [20]. The software can be set up to run any command-line driven analysis code, such as a Python script or a FE analysis code. The design study was performed within LS-OPT following the flowchart shown in Fig. 7.”) Overall, one of ordinary skill in the art would have recognized that combining Mason with Halbritter_2020 and Noevere would result in a system capable of generating more lightweight designs while still maintaining structural strength.
(5) Claims 24-25 are rejected under 35 U.S.C. 103 as being unpatentable over Automation of Process Planning for Automated Fiber Placement (Hereinafter Halbritter_2020) in view of Development of a Design for Manufacturing Tool for Automated Fiber Placement Structure (Hereinafter Noevere) in further view of Blom-Schieber (US 12240178 B1)
Claim 24. Halbritter_2020 teaches wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for using individual ply scores calculated during the ply optimization process ([Page 90 Par 2] “Major 1 has generated a normalized set of instance and severity measurements for each feature of interest. Major 2 provides a method for creating an overall ranking of many features with a series of pair-wise comparisons through the Analytical Hierarchy process. Major 3 combines the data into a single normalized score for each ply scenario. The lowest score will identify the best starting point and layup strategy of that iteration according to the thresholds and preferences used.”) ([Fig. 6.18] Shows an interface that shows the quality scores of each ply scenario within the laminate)
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Greyscale
The combination of Halbritter_2020 and Noevere does not explicitly teach create a ply summary score, describing the average quality of the collection of ply scenarios
Blom-Schieber teaches create a ply summary score, describing the average quality of a collection of plies ([Col 24 line 39-53] “The gap/lap analysis 456 determines gap data, lap data, or both, (referred to herein as “gap/lap data”) for each vector field analyzed. For example, in a particular implementation, the gap/lap data indicates locations and/or sizes of gaps, laps, or both, for one or more plies. Additionally, or alternatively, the gap/lap data summarizes gap and/or lap information for a ply or for the entire 3D model. For example, the gap/lap data indicates an average or range of overlap or gap values for a particular ply or for the entire 3D model. To illustrate, a percentage of surface area of a ply that is associated with gaps or laps can be used to indicate an average gap or lap for the ply. As another example, the gap/lap data identifies one or more locations associated with gap/lap data that exceed a threshold, such as the tow overlap threshold 122 of FIG. 1.” [Examiner’s note: measuring gap/lap locations per ply then averaging across the entire model, which is made of plies, results in a value that describes the average quality of the collection of plies])
Blom-Schieber is analogous art because it is within the field of automated fiber placement analysis and modeling. It would have been obvious to one of ordinary skill in the art to combine Blom-Schieber with Halbritter_2020 and Noevere before the effective filing date. One of ordinary skill in the art would have been motivated to make this combination in order to allow the system to produce higher quality laminates in manner that is faster and requires less rework. Blom-Schieber notes how traditional automated fiber placement systems suffer from a frequent requirement of manual rework and inspection and explains how the system disclosed therein overcomes these issues ([Col 3 line 49 – Col 4 line 58] “The layup strategy analyses disclosed herein enable production of laminates that have higher quality with less rework and faster manufacturing timelines. The disclosed analyses consider AFP layup parameters such as: a number of tows to be applied per course, a tow width, a course centerline strategy, a tow mask, ply stagger, course layup direction, course sequencing, etc. Selection of these parameters influences in-process cycle times, time spent on inspection and rework, post-processing cycle times, and laminate quality. For example, it is generally faster to manufacture a laminate by applying parallel courses with a large number of wide tows and without steering than it would be to manufacture the same laminate using non-parallel courses, fewer tows per course, narrower tows, or a different steering strategy. Further, while these parameters can be selected more or less independently in some situations, such as to manufacture a large flat object, in some circumstances the selection of one of these parameters influences the selection of one or more other parameters. For example, the selection of a steering strategy may limit the width of tows that can be used. Additionally, in many instances, the geometry of the object to be manufactured is such that parallel courses cannot be applied in a manner that follows a natural path (e.g., without steering). Further, fiber angles of applied courses may diverge from the as-designed fiber angles for various reasons, which may lead to steering of courses, nonparallel courses, or both, or narrower courses to meet fiber angle requirements. The layup strategy analyses are performed by a software tool executed by one or more processors of a computing device. During execution, the software tool evaluates manufacturability of a composite part based on a 3D model of the part generated during a part design process. The software tool generates a graphical user interface (GUI) to improve part design and AFP process decisions. For example, the GUI may include visualizations of the impact of various part design changes, AFP processing parameters, or both. The software tool uses the 3D model of the object to be manufactured and can be used early in the design process, such as before numerical control (NC) programming of AFP manufacturing tools is performed. NC programming entails generating detailed machine instructions for the AFP process, including, for example, designating course starting locations and course centerlines, which is a time consuming process. In contrast, the layup strategy analyses disclosed herein can generate output indicating best and worst case scenarios for manufacturability of the objects, indicting how changes in course width affect angle deviations and/or compaction, etc., without providing the detailed input needed for NC programming. Results of the analyses can be indicated on or with a visual depiction of the 3D model (e.g., a heat map display) to visually distinguish locations of concern. The layup strategy analyses result in better part designs and better AFP manufacturing process designs, which can reduce manufacturing time and cost. In this context, manufacturing time and cost includes not just time and expense associated with laying up fibers to form the part, but also time and expense associated with rework, inspection, and post-processing. For example, APF manufacturing sometimes results in placement of one or more tows in a course that are manually adjusted or inspected before the next course or ply is applied. Manual adjustment, inspection, and other so-called “out-of-cycle” processes do not affect “in-cycle” time of the AFP manufacturing process since the equipment is not in operation; however, these manual adjustment and inspection processes are very expensive in terms of labor costs and significantly delay the total, end-to-end manufacturing time of the part. To illustrate, by some estimates, rework and other out-of-cycle processes contribute up to 40% to the total, end-to-end manufacturing time of some parts. The disclosed layup strategy analyses enable detection of such concerns in the design stage so that different AFP manufacturing choices or part design changes can be selected to limit or avoid use of these manual adjustment and inspection processes.”) Overall, one of ordinary skill in the art would have recognized that combining Blom-Schieber with Halbritter_2020 and Noevere would result in a system that required less manual rework and inspection, ultimately increasing laminate production speed and quality.
Claim 25. Halbritter_2020 teaches wherein said one or more processors are further programmed for conducting Computer-Aided Process Planning (CAPP) for evaluating ([Page 91 Par 3 – Page 93 Par 2] “The previous sections have presented a method for generating defect measurements and ranking the importance of these defects. Major 3 will use the findings from these two previous sections to give each starting point and associated layup strategy a score. The AHP scoring provides an objective function for comparing several different layup scenarios within the CAPP module’s ranking system and reflects on the importance as assigned by individual process planners. To find the best starting point and layup strategy the program runs a procedure as follows. A localized grid of points is created near the center of the geometry over the whole layup. VCP will analyze the ply scenarios that were defined by the combination of grid points and layup strategy. Results are reimported to the CAPP, and each ply scenario is scored according to the AHP analysis and the defect measurement. Defects with higher weights have more impact on the ply scenario’s score, indicating higher amounts of that defect. The normalized scores, lower is better, can be used to select the best scenario. At this point, the current best can be used as is, or another round of iteration can be performed, using the previous best starting point and generating another starting point grid. The final score is found by multiplying the instance and severity percentages by the ranking percentages to find the overall score (Figure 4.21). The gap instances percent of .468 is multiplied by the gap rankings of .2 and then added on to the gap severity percent of .946 multiplied by .2 and so on until the final sum is found. By measuring the results of these different starting points and layup strategies an optimal scenario can be constructed for the process planners needs. The different scenarios can be easily compared, and an objective ranking of their selection criterion can be developed. The rankings can be adjusted to favor combinations of properties that affect manufacturing or the original design constraints. For manufacturing, it would be important to primarily reduce the difficult to model defects imparted by fiber steering, and to avoid gaps and overlaps which could exaggerate into defects such as wandering tows. Heeding design constraints set forth by design for loading factors, such as the ply angle, material bulk, and void content are also essential. By highly valuing angle deviation, followed by gaps and overlaps, a more optimal set of layup parameters can be selected to remain true to the design parameters set forth by the laminates structural design and loading requirements.” [Page 68 Par 3 – Page 69 Par 3] “Process planning works to minimize defects and unwanted features that will develop based on the chosen process planning parameters. … To summarize these features, two aspects of their presence must be considered. The majority of manufacturing and design processes set a maximum instance threshold for each feature. An instance threshold measures total amount of defects above a certain threshold. These limitations are imposed to reduce the propagation of more defects and to improve the manufacturability of the laminate. The other measurement used by process planners is severity. The severity threshold of the defect finds the amount that the instances above the threshold value affect the total amount of defects. This measures how significantly certain defects will affect the overall laminate and will demonstrate if there are many small defects or several large defects. The Instance functions as an indicator of commonality of the features for the given ply. The Severity of the feature uses the threshold to find the sum of the instances as they occur in the ply.” [Page 33 Par 1-2] “A gap occurs when two adjacent tows are not perfectly laid up adjacent to each other resulting in a gap between the tows. An overlap is when the two adjacent tows are overlapping onto each other. The most common cause of gaps and overlaps is steering during layup since, the tows in a course will not fit together perfectly, especially when adopting a parallel coverage strategy. However, gaps and overlaps can naturally occur outside of steering if laying up over a complex 3D tool surface. Gaps and overlaps are well anticipated in areas of purposeful steering, or on doubly curved surfaces where adequate roller compaction is not guaranteed. From the geometry of these situations, the gaps and overlaps can be directly computed. The measurement of gaps and overlaps are generally easy to detect due to the build-up or lack of material along the course. As successive plies are laid up over overlaps, consistently at the same location at each layer, significant thickness buildup will be visible. A gap and/or overlap may become a site for failure initiation under loads. Gaps would create resin rich regions for crack growth while overlaps create undulation in the fiber that can lead to compressive failures. They may also become a site for wrinkling in the layers placed over them in the succeeding layer.”)
Noevere makes obvious evaluating a plurality of respective of said laminate scenarios ([Page 3 Par 15] “The Central Optimizer process starts with optimization of ply shapes and ply counts (of each orientation) in HyperSizer. This optimization is performed on the FEM. A laminate design is selected that meets strength and buckling requirements when evaluated with the loads from the FEA solution. Additionally, design requirements such as balance and symmetry are applied to the optimization.” [Page 4 Par 3 – 8] “4. Evaluate Impact of Layup Quality The quality of the tow paths is also assessed by estimating the likelihood of defect occurrence. During the CAPP VCP iterations described previously, several layup quality metrics are checked, but the impact of those metrics was not assessed. The objective in this step is to evaluate the impact on the quality of the laminate as a whole. There are several parts of this evaluation, described below. The impact of laps and gaps on laminate strength and stiffness is assessed by mapping the lap and gap geometry to the FEM. Ply thicknesses are scaled on the FEM elements to approximate the absence of material due to gaps or the presence of extra material due to overlaps. Additionally, the thickness deviation that results from the accumulation of laps and gaps is calculated. … This physics-based process model is capable of predicting porosity that occurs during the cure cycle due to local changes in resin pressure. These porosity predictions inform the rest of the Central Optimizer process of changes that could be necessary to the laminate design or even the part geometry. 5. Iterate to Achieve Satisfactory Design The challenge of generating a satisfactory design is that significant coupling exists between the analyses discussed above. This requires iteration between the tools to produce a laminate that meets both manufacturing and structural performance requirements. For example, a set of tow paths that produces positive strength margins may require tow steering that creates an unacceptable number of defects. Conversely, another set of tow paths that has low steering could result in negative strength margins, a high number of laps and gaps, etc. It is necessary to evaluate every aspect of the laminate quality each time a change is made to the design (tow paths, ply boundaries, etc). These competing objectives can make it challenging to choose a single design that perfectly satisfies every objective in each discipline. Thus, it is often necessary to choose a laminate design that is a compromise between the disciplines. The objective of iterating the laminate design is to find the best possible compromise. This solution will vary depending on program design requirements or manufacturer preferences.” [Examiner’s note: iterating. modifying, and evaluating various laminate designs is equivalent to evaluating a plurality of laminate scenarios])
Allowable Subject Matter
Additionally, Claims 28-29, 31, and 33-38 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims in a way that overcomes the previously outlined rejection under 35 U.S.C. 101 and 112.
Claims 28-29 would be allowed under 35 U.S.C. 103 over prior art. The following is a statement of reasons for the indication of allowable subject matter: prior art representative of the claim, in particular specifically exporting course to course overlaps from Vericut Composite Programming as STEP files that define each gap and overlap instance specifically as a unique closed contour, where fiber angle deviation and steering are defined on a regular grid over a tool surface, as in claim 28, could not be found.
Claim 31 would be allowed under 35 U.S.C. 103 over prior art. The following is a statement of reasons for the indication of allowable subject matter: prior art representative of the claim, particularly the specific ply processing sequence coupled with defect interaction level modification could not be found.
Claims 33-38 would be allowed under 35 U.S.C. 103 over prior art. The following is a statement of reasons for the indication of allowable subject matter: prior art representative of the claim, particularly the specific defect level- and area- based frequency score equation used in claim 33 could not be found.
Conclusion
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/M.P.M./Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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