DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is made non-final.
Claims 1-16 filed on 09/12/2024 have been reviewed and considered by this office action.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on Application No. EP22162312.7 filed on 03/15/2022. Copies of certified papers required by 37 CFR 1.55 have been received. Priority is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Information Disclosure Statement
The information disclosure statement filed on 09/12/2024 has been reviewed and considered by this office action.
Drawings
The drawings filed on 09/12/2024 have been reviewed and are considered acceptable.
Specification
The specification filed on 09/12/2024 has been reviewed and is considered acceptable.
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 3-7, 15, and 16 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.
Regarding claims 3, 5, and 15, the phrase “such as” renders the claims indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d). For the purpose of examination, the claims will be interpreted such that these limitations are optional and not required, but the claims must be amended to remove the language or to make clear what limitations are covered by the claim scope.
Regarding claims 4 and 5, the claims recite the limitation “a set of optimized process parameters is selected within the acceptance region.” There is insufficient antecedent basis for this limitation in the claim. For the purpose of examination, Examiner interprets this to mean “a process parameters' window yielding an acceptable quality factor,” as it appears that the Applicant intended to incorporate the feature of claim 1 that recites the antecedent basis of “the acceptance region.”
Regarding claims 6 and 7, the phrase “preferably at least” renders the claims indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d). For the purpose of examination, the claims will be interpreted such that these limitations are optional and not required, but the claims must be amended to remove the language or to make clear what limitations are covered by the claim scope.
Regarding claim 15, the phrase “possibly by interpolation” renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d). For the purpose of examination, the claim will be interpreted such that interpolation between the process parameters is optional and not required, but the claim must be amended to remove the language or to make clear what limitations are covered by the claim scope.
Claims 4 and 16 are rejected due to their dependency upon rejected claims and are rejected for the same reasons as outlined above.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1, 5, 6, 8, and 9 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gupta et al. (US 2020/0298499 A1).
Regarding claim 1, Gupta discloses a method for optimizing one or more process parameters of an additive manufacturing process, comprising, prior to the step of manufacturing at least one AM part, the steps of:
a. generating, on a computing device, a 3D digital model of a set of test samples to be manufactured by the additive manufacturing process ([0025]: “The user-specified geometry may be received via CAD file, or other suitable file”), wherein each test sample has a predetermined geometry ([0023]: “Coupons may be simple shapes suitable for analysis that may contain one or more features and may be representative of the parts being built. For example, the features may include, but are not limited to, bulk, contour, thin-walls, downside (over-hangs of different angles), through-holes (for dimensional tolerances), and upside (top surface of the part, dome, etc.)”);
b. manufacturing said set of test samples on a build platform of an additive manufacturing system, each test sample being monitored by at least one electromagnetic sensor as it is being manufactured according to the 3D digital model ([0032]: “In-situ monitoring tool data 504 is then collected from the one or more monitoring system 120 of the AMM 114 for each of the parts in S214, as shown in FIG. 5B”; [0011]: “sensors may be a non-exhaustive example of an in-situ monitoring system”; [0041]: “ entries 602 may include in-situ monitoring system response vs. test measurements (e.g., photo-diode intensity vs. porosity),” where a photo-diode/in-situ optical sensor senses electromagnetic radiation), wherein each test sample is made up of two or more test coupons manufactured sequentially, for obtaining at least one optimized set of process parameters ([0031]: “As shown in FIGS. 3B and 3C, each stack 306 may include one or more parts (coupons) 304, where each part may include one or more layers 303… while only five parts in a stack are shown herein, any number of parts may be used to form a coupon/stack”), and
c. storing said at least one optimized set of process parameters for subsequent use during the building process of said at least one AM part, which is representative of a test coupon of a given acceptable quality factor ([0039]: “the optimal (e.g., down-selected) parameters in the parameter sets may be added to the library 122 for future implementation and investigations”; [0044]: “the library entries (e.g., set of build parameters) may be used to control the AMM to fabricate the parts”),
wherein said at least one optimized set of process parameters is obtained by carrying out the following steps: i. manufacturing at least two test coupons with two different sets of guess process parameters ([0035]: “One or more power and speed combinations within the machine's operating range are used as the initial set of build parameters 112”),
ii. sensing said two test coupons with said at least one electromagnetic sensor ([0032]: “Continuing with the stacks/parts shown in FIG. 3B, 'a' and 'b' may be fabricated in S212 as shown in FIG. 5A. In-situ monitoring tool data 504 is then collected from the one or more monitoring system 120 of the AMM 114 for each of the parts in S214, as shown in FIG. 5B. In one or more embodiments, the collected in-situ monitoring data may be for at least one of the one or more layers 303 or the one or more parts 304”; [0042]: “As a non-exhaustive example, the build plate 302 includes 50 parts, and if two parts are fabricated from the first set of build parameters, 100 build parameter sets (DOEs) may be analyzed via analysis of the parts”),
iii. changing for each new coupon at least one process parameter as a function of a) the previous sets of process parameters for the respective built test coupons, and b) the quality factor of respective test coupons measured by the electromagnetic sensor to obtain respective new sets of process parameters ([0011]: “The in-situ monitoring data collected from previous builds, stacks, parts or layers may be used to inform the build parameter sets for subsequent builds, stacks, parts or layers. For example, build parameters informed by an in-situ monitoring system may be changed for progressive layers on some or all parts, without interrupting the build and without the need for physical testing (i.e., without extracting parts from the build for ex-situ non-destructive evaluation or cut-ups and characterization) or mechanical testing, or any other measurements”),
iv. manufacturing an additional set of test coupons of respective test samples with said respective new set of process parameters ([0039]: “After S222, the process returns to S212, and one or more additional parts are fabricated”; [0042]: “build parameter sets (DOEs) may be analyzed via analysis of the parts. Based on that analysis in S218, only 4 build parameter sets may be down-selected to have 'part c' and 'part d' fabricated”),
v. sensing the additional set of test coupons with said at least one electromagnetic sensor ([0043]: “The corrections may be based on data from sensors (or other in-situ monitoring systems)”),
vi. repeating steps iii. to v. until the predetermined geometry of each test sample is obtained ([0038]: “When in S218, the predefined criteria are met, the process 200 ends in S220”' [0043]: “this may be an iterative process using in-situ monitoring data, where variable corrections or adjustments are applied to the parameters of a first set for predictable disturbances (e.g., to correct laser power level as a function of laser position) in the generation of the next set of build parameters”),
wherein a process parameters' window yielding an acceptable quality factor for said at least one AM part is computed as a function of the sets of process parameters used for building the test coupons of respective test samples, and the quality factor of said test coupons ([0040]: “in-situ monitoring system data analysis from previous builds/coupons may be used to determine the operating window and down-selected parameters that may be used as a starting point for optimization of different feature types”; [0041]: “The library 122 of response surfaces is generated by mapping the executed analysis of the in-situ monitoring data to one or more test measurements and/or results”), and
wherein said at least one optimized set of process parameters is selected within said process parameter window ([0043]: “the next set of build parameters 126 may be informed by the first set of build parameters (or any immediately preceding build parameter sets or processing window) in combination with any predictive material models”; [0030]: “the conventional serial process is often used because it may be necessary to down-select an operating window (which is usually larger for) bulk sections, followed by further refinement for other complex features. It may be a linear process where knowledge/data/parameter sets/measurements from previous steps inform the next step as the complexity of the features increase”).
Regarding claim 5, Gupta discloses the method of claim 1.
Gupta further discloses wherein a set of optimized process parameters is selected within the acceptance region based on productivity criteria such as the coupon manufacturing time ([0009]: “Embodiments provide for optimizing a parameter development process and machine parameters for building the part, with not only a high-quality but also with optimal production rate (e.g., quality, performance, cost and speed). Embodiments provide for optimization for alone or more metrics”).
Regarding claim 6, Gupta discloses the method of claim 1.
Gupta further discloses wherein each test sample of the set of test samples comprises at least three test coupons, preferably at least five test coupons arranged on top of each other ([0031]: “while only five parts in a stack are shown herein, any number of parts may be used to form a coupon/stack”).
Regarding claim 8, Gupta discloses the method of claim 1.
Gupta further discloses comprising manufacturing on the build platform a first test sample comprising a plurality of test coupons of different shapes arranged next to each other and manufacturing at least one additional test sample having a corresponding plurality of test coupons of shapes identical to the shapes of respective plurality of test coupons of the first test sample ([0031]: “One part ('a') 304 in FIG. 3B, may represent the bulk or core features. Another part ('b') 304 in FIG. 3B, may represent contour features. Other parts 304 in FIG. 3B, may represent through horizontal holes (e.g., 0.5, 1, 2, 3 mm), etc.), wall features (e.g., varied wall thicknesses (e.g., 0.5, 1, 2, 3 mm), downside surface angles (e.g., 0°, 30°, 60°)/upside surfaces/angles, respectively”; [0030]: “ a 'build' may be parallel such that multiple parts with multiple features are fabricated on a single build plate”),
wherein step b. of claim 1 comprises: i. manufacturing the plurality of test coupons of a first test sample with a unique set of process parameters per test coupon ([0042]: “100 build parameter sets (DOEs) may be analyzed via analysis of the parts”);
ii. sensing each test coupon of said first test sample with said at least one electromagnetic sensor ([0032]: “In-situ monitoring tool data 504 is then collected from the one or more monitoring system 120 of the AMM 114 for each of the parts in S214, as shown in FIG. 5B”), and
iii. changing at least one process parameter of each unique set of process parameters per coupon as a function of the data sensed for the corresponding test coupon to obtain a new set of process parameters for each test coupon ([0043]: “variable corrections or adjustments are applied to the parameters of a first set for predictable disturbances (e.g., to correct laser power level as a function of laser position) in the generation of the next set of build parameters”).
Regarding claim 9, Gupta discloses the method of claim 1.
Gupta further discloses wherein several optimized sets of process parameters are obtained during a single build cycle of the set of test samples ([0009]: “ parameters for different geometries or features may be optimized in parallel based on an in-situ response plus the predictive material model”), each optimized set of process parameters being assigned to a specific geometry type ([0011]: “These input and output variables may be presented as multi-dimensional plots of material anomalies, surface finish, physical or mechanical properties or microstructures as a function of laser parameters (e.g., power, speed, spot-size and hatch-spacing, for each geometric feature (e.g., bulk, thin-wall/skin, thru-holes, down-side/upside surfaces)”) and stored for subsequent use for building a portion zone of corresponding geometry of said at least one AM part ([0043]: “the iterative process may include optimization of build parameters for one or more geometric feature types (e.g., bulk only, bulk and contour, or bulk, contour, thin-walls, etc.)”; [0044]: “ the library entries (e.g., set of build parameters) may be used to control the AMM to fabricate the parts”).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (US 2020/0298499 A1), in view of Aoyagi et al. (US 2021/0299754 A1).
Regarding claim 2, Gupta discloses the method of claim 1.
While Gupta teaches refining parameter sets from a prior processing window ([0040]: “in-situ monitoring system data analysis from previous builds/coupons may be used to determine the operating window and down-selected parameters that may be used as a starting point for optimization of different feature types”; [0043]: “the next set of build parameters 126 may be informed by the first set of build parameters (or any immediately preceding build parameter sets or processing window)”), Gupta does not explicitly teach “wherein said respective new set of process parameters is obtained under step iii. by resampling a zone of the process parameters around the extremum of the quality factor derived from the electromagnetic sensor, or a zone where the quality factor derived from the electromagnetic sensor is above a given threshold.”
Aoyagi further teaches wherein said respective new set of process parameters is obtained under step iii. by resampling a zone of the process parameters around the extremum of the quality factor derived from the electromagnetic sensor, or a zone where the quality factor derived from the electromagnetic sensor is above a given threshold ([0074]: “the data points are set in a process window including the vicinity of the classification boundary”; [0125]: “the selection region of parameter sets in the original process window may be limited to a region including the boundary”; [0189]: “ if a parameter set is close to a boundary, the adjusted parameter set generator 2021 performs adjustment such that the parameter set is apart from the boundary. This may be done based on comparison between a threshold and the distance of the parameter set from the boundary”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the method of Gupta to incorporate the teachings of Aoyagi so as to include said respective new set of process parameters being obtained under step iii. by resampling a zone of the process parameters around the extremum of the quality factor derived from the electromagnetic sensor, or a zone where the quality factor derived from the electromagnetic sensor is above a given threshold. Doing so would allow the process window to be refined using fewer experiments with the aim of reducing cost and time (Aoyagi, [0034]: “it is possible to construct a general-purpose process window while saving cost and time”).
Claims 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (US 2020/0298499 A1), in view of Lourakis (Lourakis, Manolis IA. “A brief description of the Levenberg-Marquardt algorithm implemented by levmar.” Foundation of Research and Technology 4.1 (2005): 1-6.).
Regarding claim 3, Gupta discloses the method of claim 1.
Gupta further teaches wherein a multivariate function of the quality factor of the test coupons of respective test samples as function of their respective sets of process parameters is obtained from the electromagnetic sensor data ([0011]: “an outcome may also comprise a build parameter response surface (build parameters vs. material properties) that may be used to generate build parameters for other applications with different objectives (e.g., trade-off between productivity and quality, or one property, e.g., creep vs. another property, e.g. low cycle fatigue)”; [0041]: “The library 122 of response surfaces is generated by mapping the executed analysis of the in-situ monitoring data to one or more test measurements and/or results. The entries 602 may include in-situ monitoring system response vs. test measurements (e.g., photo-diode intensity vs. porosity); laser parameters vs. test measurements (e.g., power, speed vs. porosity); or laser parameters vs. in-situ monitoring system response (e.g., power, speed vs. photo-diode intensity). Other suitable entries may be used”).
While Gupta teaches using a multi-objective optimization algorithm to find new parameter sets ([0012]: “One or more embodiments may provide a library of one or more data sets of build parameters and resulting properties, and thus, provide flexibility to the user for multi-objective optimization e.g., high-quality vs. high productivity build parameter sets”), Gupta does not explicitly teach “wherein an optimization algorithm such as the steepest descent algorithm, the conjugate gradient algorithm or the Monte-Carlo algorithm is used on the multivariate function to find said new sets of process parameters.”
Lourakis further teaches wherein an optimization algorithm such as the steepest descent algorithm, the conjugate gradient algorithm or the Monte-Carlo algorithm is used on the multivariate function to find said new sets of process parameters (Page 1, Introduction: “The Levenberg-Marquardt (LM) algorithm is an iterative technique that locates the minimum of a multivariate function that is expressed as the sum of squares of non-linear real-valued functions… LM can be thought of as a combination of steepest descent and the Gauss-Newton method”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the method of Gupta to incorporate the teachings of Lourakis so as to include an optimization algorithm such as the steepest descent algorithm, the conjugate gradient algorithm or the Monte-Carlo algorithm being used on the multivariate function to find said new sets of process parameters. Doing so would allow the optimizer to converge on a solution found with the aim of finding optimal sets of process parameters (Lourakis, Page 1, Introduction: “When the current solution is far from the correct one, the algorithm behaves like a steepest descent method: slow, but guaranteed to converge”).
Regarding claim 4, Gupta in view of Lourakis teaches the method of claim 3.
Lourakis further teaches wherein a set of optimized process parameters is selected within the acceptance region where the gradient of said multivariate function is below a given threshold (Page 3, The Levenberg-Marquardt Algorithm: “The LM algorithm terminates when at least one of the following conditions is met: The magnitude of the gradient of
ϵ
T
ϵ
, i.e.
J
T
ϵ
in the right hand side of Eq. (2), drops below a threshold
ε
1
”).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (US 2020/0298499 A1), in view of Bamberg et al. (US 2014/0159266 A1).
Regarding claim 7, Gupta discloses the method of claim 1.
While Gupta teaches that each test coupon is made up of more than one layer ([0031]: each part (coupon) “may include one or more layers 303”), Gupta does not explicitly teach “wherein each test coupon, is made up of at least five layers, preferably at least ten layers such that the thickness of the coupon is larger than the penetration depth of the electromagnetic field generated by said at least one electromagnetic sensor.”
Bamberg further teaches wherein each test coupon, is made up of at least five layers, preferably at least ten layers such that the thickness of the coupon is larger than the penetration depth of the electromagnetic field generated by said at least one electromagnetic sensor ([0006]: “an eddy-current scan of the solidified region is generated, whereby a scanning depth corresponds to a multiple of the layer thickness”; [0010]: “of approximately 0.5 mm is achieved, by which means approximately 10 to 20 material layers are scanned simultaneously”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the method of Gupta to incorporate the teachings of Bamberg so as to include wherein each test coupon, being made up of at least five layers, preferably at least ten layers such that the thickness of the coupon is larger than the penetration depth of the electromagnetic field generated by said at least one electromagnetic sensor. Doing so would allow non-destructive testing with the aim of reducing complexity, time, and cost (Bamberg, [0002]: “ Destructive tests, however, require the production of a large number of components or test specimens. Non-destructive test methods such as, e.g., x-ray computed tomography, are technically complex, time-consuming and costly”).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (US 2020/0298499 A1), in view of Guo et al. (Guo, Sai, Guanhui Ren, and Bi Zhang. “Subsurface defect evaluation of selective-laser-melted inconel 738LC alloy using eddy current testing for additive/subtractive hybrid manufacturing.” Chinese Journal of Mechanical Engineering 34.1 (2021): 111.).
Regarding claim 10, Gupta discloses the method of claim 1.
While Gupta teaches a threshold defect fraction value ([0035]: “the threshold on which the system is optimized may be a value for an acceptable defect fraction”), Gupta does not explicitly teach “wherein the test coupon of an acceptable quality level comprises a bulk density of at least 99%.”
Guo further teaches wherein the test coupon of an acceptable quality level comprises a bulk density of at least 99% (Page 4, Section 2.1: “The mass density of the SLMed bulk Inconel 738LC alloy sample was tested to be higher than 99 %, only micro defects (average defect size < 20 μm) randomly scattering in a sample as shown in Figure 3(b)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the method of Gupta to incorporate the teachings of Guo so as to include the test coupon of an acceptable quality level comprises a bulk density of at least 99%. Doing so would allow a quality threshold to be expressed using a bulk density value with the aim of setting an objective quality level for additive manufacturing material (Guo, Page 3, Section 1: “SLMed parts should to be highly dense and stable”).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (US 2020/0298499 A1), in view of Dehghan et al. (US 2018/0036964 A1).
Regarding claim 11, Gupta discloses the method of claim 1.
Gupta does not explicitly teach “wherein said at least one electromagnetic sensor is an eddy-current sensor.”
Dehghan further teaches wherein said at least one electromagnetic sensor is an eddy-current sensor ([0049]: “The eddy current scan 921 uses an eddy current scanner to scan the area of the build platform 112 on which the structure/part 140 is built”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the method of Gupta to incorporate the teachings of Dehghan so as to include said at least one electromagnetic sensor being an eddy-current sensor. Doing so would allow eddy-current electromagnetic scanning as the test coupon is constructed with the aim of improving efficiency and reducing cost ([0065]: "Non-destructive testing methods that have to be done to inspect each part in the past can now be eliminated using the inventive method and system, since the part/structure is inspected/assessed as it is constructed. Non-destructive testing of completed 3D parts may be undesirable because, it is very difficult to perform NDT on the parts due to complex geometry, and complex material properties, and computed tomography (CT) is very time consuming, costly and has other disadvantages. In addition, if NDT is performed after the part/structure is built, and then it is decided to scrap the part, then much time has been lost").
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (US 2020/0298499 A1), in view of Dehghan et al. (US 2018/0036964 A1), and in view of Aoyagi et al. (US 2021/0299754 A1).
Regarding claim 12, Gupta discloses the method of claim 1.
Gupta does not explicitly teach “wherein a calibration sample of known density, conductivity or with known defects is measured at least once within the additive manufacturing system, the method further comprising the step of computing a lower bound and upper bound of said process parameters' window as a function of the raw electromagnetic sensor values obtained when sensing said calibration sample.”
Dehghan further teaches wherein a calibration sample of known density, conductivity or with known defects is measured at least once within the additive manufacturing system ([0048]: “Calibration blocks 170 are provided to have an accurate and repeatable test for each layer, to permit modification of scanning characteristics, such as distance, frequency and etc. to optimize the sensitivity of the scanner/sensors, and to use known defects with known sizes so that the system can use their data for sizing and defect classification”).
The reasons to combine Dehghan into Gupta are the same as articulated in the rejection of claim 11 above.
Dehghan does not explicitly teach “the method further comprising the step of computing a lower bound and upper bound of said process parameters' window as a function of the raw electromagnetic sensor values obtained when sensing said calibration sample.”
Aoyagi further teaches the method further comprising the step of computing a lower bound and upper bound of said process parameters' window as a function of the raw electromagnetic sensor values obtained when sensing said calibration sample ([0070]: “a process window including a range from the maximum value to the minimum value of each parameter settable by the laminating and shaping unit is used as the initial process window”; [0100]: “The scattered parameter set generator 511 generates scattered parameter sets in a process window generated by the process window generator 512 such that a boundary can be determined from the evaluation results of samples. The process window generator 512 initially uses an initial process window 531 held in the database 513, and in subsequent repeat, generates a region including a boundary determined by the boundary determiner 517 as a new process window. The database 513 holds the initial process window 531, an updating process window 532, and an adjusted process window 533”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the method of Gupta in view of Dehghan to incorporate the teachings of Aoyagi so as to include the method further comprising the step of computing a lower bound and upper bound of said process parameters' window as a function of the raw electromagnetic sensor values obtained when sensing said calibration sample. Doing so would allow the process window to be refined using fewer experiments with the aim of reducing cost and time (Aoyagi, [0034]: “it is possible to construct a general-purpose process window while saving cost and time”).
Claims 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (US 2020/0298499 A1), in view of Alberts et al. (US 2023/0141266 A1).
Regarding claim 13, Gupta discloses the method of claim 1.
While Gupta teaches determining optimal process parameters corresponding to a specific position ([0043]: “to correct laser power level as a function of laser position… The iterative process may determine a correlation between sensor data and part quality”), Gupta does not explicitly teach “wherein once said at least one optimized set of process parameters has been obtained, a mapping of the build platform is generated as a function of a specific quality for a multitude of areas on the build platform corresponding to the specific position of each test sample on the build platform.”
Alberts further teaches wherein once said at least one optimized set of process parameters has been obtained, a mapping of the build platform is generated as a function of a specific quality for a multitude of areas on the build platform corresponding to the specific position of each test sample on the build platform ([0120]: “The input class method describes the sum of a selection of machine-specific influencing variables occurring before, during and after the construction process. Emphasis is placed, in this example, on the in-process input parameters with the process parameters such as laser power or deflection speed, the scan strategy such as position data or vector lengths or geometry-specific dependencies”; [0134]: “an individual topology optimization of the geometry, the component position and component orientation on the building platform as well as the parameters to be used can be adapted, modified and optimized”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the method of Gupta to incorporate the teachings of Alberts so as to include once said at least one optimized set of process parameters has been obtained, a mapping of the build platform being generated as a function of a specific quality for a multitude of areas on the build platform corresponding to the specific position of each test sample on the build platform. Doing so would allow the parameter mapping to be adapted to particular areas on the build platform with the aim of improving reliability (Alberts, [0007]: “Selective Laser Melting includes numerous and complex variables like build part placement and orientation, process parameters as well as machine and environmental condition makes it challenging to ensure stabile and reliable conditions, to produce final parts with repeatable quality features which reach their assumed quality requirements”).
Regarding claim 14, Gupta in view of Alberts teaches the method of claim 13.
Gupta does not explicitly teach “wherein said mapping is used to design a build platform layout excluding zones, on the build platform which have shown to produce test coupons of lesser quality in comparison with other test coupons, for additive manufacturing of said at least one AM part.”
Alberts further teaches wherein said mapping is used to design a build platform layout excluding zones, on the build platform which have shown to produce test coupons of lesser quality in comparison with other test coupons, for additive manufacturing of said at least one AM part ([0133]: “a partial cancellation of a construction process could be possible, parts that are supposed to be defective are cancelled, whilst non defective parts of the build job would be finished. The simulation can be adapted online for a change in the parameters due to the partial cancellation”).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (US 2020/0298499 A1), in view of Alberts et al. (US 2023/0141266 A1), and in view of Khairallah et al. (US 2019/0291348 A1).
Regarding claim 15, Gupta in view of Alberts teaches the method of claim 13.
Gupta further teaches wherein said mapping is used to compensate for the inhomogeneity of the build process over the build platform by determining optimal position-dependent sets of process parameters such as laser scan speed, as a function of the position on the build platform, possibly by interpolation between the process parameters of the mapping ([0043]: “this may be an iterative process using in-situ monitoring data, where variable corrections or adjustments are applied to the parameters of a first set for predictable disturbances (e.g., to correct laser power level as a function of laser position) in the generation of the next set of build parameters”),
While Gupta teaches determining parameters as a function of position, Gupta and Alberts do not explicitly teach “wherein said optimal position-dependent sets of process parameters are transferred in an electronic format to a machine controller of the additive manufacturing system so that real AM parts are built with the corrected optimized set of process parameters corresponding to their X-Y position of the build plate to reach a uniform quality for all positions on the build platform.”
Khairallah further teaches wherein said optimal position-dependent sets of process parameters are transferred in an electronic format to a machine controller of the additive manufacturing system so that real AM parts are built with the corrected optimized set of process parameters corresponding to their X-Y position of the build plate to reach a uniform quality for all positions on the build platform ([0063]: “The inventors' apparatus, systems, and methods produce a power map that describes laser power changes as the laser beam moves along a path to create the product or part. The map shown is a fractional power versus time. The time is related to the laser's position on the build plate in time”; [0005]: “the concept of producing maps is not limited to power maps, as other control strategies allow to control the laser speed, beam width and or laser speed and laser power at the same time”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the method of Gupta to incorporate the teachings of Khairallah so as to include said optimal position-dependent sets of process parameters being transferred in an electronic format to a machine controller of the additive manufacturing system so that real AM parts are built with the corrected optimized set of process parameters corresponding to their X-Y position of the build plate to reach a uniform quality for all positions on the build platform. Doing so would allow additive manufacturing parameters to be changed with respect to position with the aim of improving quality (Khairallah, [0031]: “When the power map is adopted by an AM machine, the laser power is changed appropriately to prevent defects”).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (US 2020/0298499 A1), in view of Alberts et al. (US 2023/0141266 A1), in view of Khairallah et al. (US 2019/0291348 A1), and in view of Roychowdhury et al. (US 2020/0081414 A1).
Regarding claim 16, Gupta in view of Alberts and Khairallah teach the method of claim 15.
Gupta, Alberts, and Khairalah do not explicitly teach “wherein the step b. is carried out on different additive manufacturing machines or on the same machine at different points in time, and wherein the differences in quality level or differences in quality factor between the test samples are used to classify the different machines as a function of the quality of the metal pieces that are manufactured in said machines or to detect a variation in the machine performance between two different times.”
Roychowdhury further teaches wherein the step b. is carried out on different additive manufacturing machines or on the same machine at different points in time ([0012]: “Embodiments generate a nominal machine-specific fingerprint from sensor data obtained while the machine is in its factory-new and/or in a suitable operational condition”; [0021]: “the additive machine can begin its production run. At some future point in time (after some usage of the machine), a user can decide to verify the calibration of the additive machine”), and
wherein the differences in quality level or differences in quality factor between the test samples are used to classify the different machines as a function of the quality of the metal pieces that are manufactured in said machines or to detect a variation in the machine performance between two different times ([0013]: “current sensor data is recorded to obtain a current machine fingerprint. A comparison between the current and the nominal machine fingerprints can be performed to quantify magnitude differences between parameters of the fingerprints. These magnitude differences are used by an embodying additive machine calibration algorithm to estimate the machine's operational drift/shift, which can be used to automatically adjust and/or update calibration files and tables in the additive machine”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the method of Gupta in view of Khairallah to incorporate the teachings of Roychowdhury so as to include the step b. being carried out on different additive manufacturing machines or on the same machine at different points in time, and wherein the differences in quality level or differences in quality factor between the test samples are used to classify the different machines as a function of the quality of the metal pieces that are manufactured in said machines or to detect a variation in the machine performance between two different times. Doing so would allow optimal additive manufacturing parameters to be updated with the aim of maintaining consistent material properties while improving efficiency (Roychowdhury, [0006]: “To ensure that the machine is in acceptable state so that the same command will generate same material properties within acceptable tolerance, conventionally a re-calibration is performed at regular intervals. Conventional recalibrations are time consuming (days to weeks) depending on the machine factors being recalibrated. Moreover, conventional additive machine recalibration is strongly susceptible to operator-induced variation, as the recalibration accuracy is significantly dependent on the expertise of the operator and the operator's familiarity with the machine being recalibrated”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2018/0370144 A1: Execution of scan instructions by an additive manufacturing controller
US 2020/0223144 A1: Interpolation from spatial build platform mapping values
US 2022/0105569 A1: Site-specific controller setpoints for additive manufacturing
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/M.I.K./Examiner, Art Unit 2117
/ROBERT E FENNEMA/Supervisory Patent Examiner, Art Unit 2117
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