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 .
This Action is non-final and is in response to the claims filed December 15, 2023 via preliminary amendment. Claims 1-15 are currently pending, of which claims 1-15 are currently amended.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. Examiner encourages language that includes purported inventive concepts, such as training models and/or the digital twins.
Claim Objections
Claims 1 and 15 are objected to because of the following informalities:
Claim 1 is objected to because it recites two instances of “and” in lines 15 and 27. Using “and” is reserved for the penultimate limitation of a claim (or a claim hierarchy). The “and” in line 15 should be removed, or the indentation of the claim should be amended to group features together where both “and” statements would be following the penultimate limitation in each of their respective claim limitation hierarchies. The claim further recites “the detection models” and, to avoid confusion and to ensure uniform claim language, the claim should separately refer to both the generalized anomaly detection model and the adaptive anomaly detection model. Claim 15 recites similar language and is objected to for at least the same reasons therein.
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 2, 9, and 10 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.
Claim 2 recites “an adaptive anomaly detection model” and it is unclear if this is a new model or the same model as introduced in claim 1.
Claim 9 recites “the observation time point” and there is insufficient antecedent basis for this limitation in the claim.
Claim 10 recites “the point cloud” and there is insufficient antecedent basis for this limitation in the claim.
Claim Interpretation
Claim 2 is/are directed to a method that recites “when the process parameter is reused”. The conditional nature of this claim language allow for an interpretation where any prior art meets the broadest reasonable interpretation of the claim without having the “storing” limitation of claim 2. See MPEP 2111.04(II); see also Ex parte Schulhauser.
Examiner’s Note
The prior art rejections below cite particular paragraphs, columns, and/or line numbers in the references for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art.
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.
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.
Claim(s) 1-6 and 15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mehr et al. (U.S. Publication No. 2018/0341248; hereinafter “Mehr”).
As per claim 1, Mehr teaches a method for the additive manufacture of a component, the method comprising
creating a machine code (See Mehr paras. [0034], [0118], and [0165]: process control instructions executed);
creating a representation of a generalized anomaly detection model; training the representation with training data originating from datasets of sensor data from a previously executed manufacturing process with a known process result (See Mehr para. [0133-134]: initial model provided via an input design geometry for an object; para. [0131]: sensor data can be used with a reference data set);
calculating output data from input data (See Mehr para. [0052]: prior to deposition, process parameters are chosen; paras. [0133]: input design geometry); and
creating an adaptive anomaly detection model trained on a parameter-set-specific basis with available training data (See Mehr paras. [0124-125]: simulation data used and trained to create defect classification models);
transferring the machine code and the detection models to a control system; starting the manufacturing process; monitoring the process with sensors (See Mehr Fig. 13 and paras. [0022] and [0111-112]: monitoring the process in real-time using a variety of sensors. The deposition process begins and process control instructions may be shared and exchanged);
evaluating sensor signals of the manufacturing process using the generalized anomaly detection model (See Mehr paras. [0111-112] and [0125]: “herein the real-time data from the one or more sensors is provided as input to the machine learning algorithm and allows the classification of detected object defects to be adjusted in real-time”);
training a specialized anomaly detection model in parallel from an adaptive anomaly detection model using process data of the running manufacturing process (See Mehr paras. [0134]: “the training data set may be updated in real-time using process simulation data, process control data, process characterization data, in-process inspection data, and/or post-build inspection data as fabrication is performed on a given system”); and
detecting anomalies in the manufacture of the component using the specialized anomaly detection model during the manufacturing process (See Mehr paras. [0131-132] and [0137]: “the real-time process characterization data that is fed to the machine learning algorithm used to run process control may comprise data supplied by an automated object defect classification system as described above, so that the deposition process control parameters may be adjusted in real-time to compensate or correct for part defects as they arise during the build process”).
As per claim 2, Mehr further teaches the method as claimed in claim 1, further comprising:
storing an adaptive anomaly detection model trained based on a process parameter set as a specialized anomaly detection model (See Mehr paras. [0133-134]: employing specific type of machine learning algorithm with parameters associated with an optimal control sequence, where the parameters can be adjusted in real-time; paras. [0168-169]: storage units); and
when the process parameter set is reused, using the specialized anomaly detection model (See Mehr Fig. 8 and para. [0178]: predicted future build states based on current build state and a set of actions).
As per claim 3, Mehr further teaches the method as claimed in claim 1, further comprising using the specialized anomaly detection model at a start of a second manufacturing process with a second process parameter set; and training the specialized anomaly detection model with the adaptive anomaly detection model (See Mehr Fig. 8 and para. [0178]: predicted future build states based on current build state and a set of actions; paras. [0126-127]: “the training data set may be updated in real-time with object defect and object classification date as it is performed on a given system. In some instances, the training data may be updated with object defect data and object classification data drawn from a plurality of automated defect classification systems”; para. [0027]: models can be deployed across multiple workspaces and work sites).
As per claim 4, Mehr further teaches the method as claimed in claim 1, further comprising:
developing a digital twin of the resulting component in parallel during the process from the sensor data comprising position data of detected anomalies (See Mehr paras. [0032], [0139], and [0178]: compare current state against target fabrication data/state in real-time. This is a digital twin);
predicting via a position of a printhead at a particular time using the machine code (See Mehr paras. [0116-118]: predicting next actions of the deposition process, including the indicated positions of the wire feed (i.e., the print head));
analyzing a working area around the position based on anomalies present in the digital twin (See Mehr paras. [0125-126] and [0129]: sensors to monitor used in the defect classification); and
adjusting the process parameters on reaching the working area for the elimination of the anomaly (See Mehr para. [0124]: “determine a set or sequence of process control parameter adjustments that will implement a corrective action, e.g., to adjust a layer dimension or thickness, so as to correct a defect when first detected”).
As per claim 5, Mehr further teaches the method as claimed in claim 4, further comprising introducing the anomalies determined by the anomaly detection models into the digital twin (See Mehr paras. [0125-126]: training sets using simulated data and real-time sensor data to classify defects in real-time).
As per claim 6, Mehr further teaches the method as claimed in claim 4, further comprising assigning a timestamp to each position approached by the printhead (See Mehr paras. [0108] and [0139]: parameters can include “the location of a deposition apparatus as a function of time” while the current state is compared to the design target and adjust the control parameters accordingly).
As per claim 15, Mehr teaches an additive manufacturing device that implements the same features as the method of claim 1, and is therefore rejected for at least the same reasons therein. Furthermore, Mehr teaches a robot arm; a control system; a printhead; sensors to detect process parameters; wherein the control system is configured to implement said method (See Mehr paras. [0034], [0051], [01112], and [0117]).
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.
Claims 7 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mehr as applied above, and further in view of Sha et al. (U.S. Publication No. 2022/0285009; hereinafter “Sha”).
As per claim 7, Mehr teaches the method as claimed in claim 4. However, while Mehr teaches the digital twin as well as points in a coordinate system (See Mehr paras. [0084] and [0095]), Mehr does not explicitly teach a point cloud.
Sha teaches wherein digital twin includes a point cloud and an anomaly value is determined for each point by means of one of the anomaly detection models for which a process state is stored (See Sha paras. [0088], [0100], and [0178]: point cloud used to determine an amount of error, where values are provided).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the digital twin and defect detection of Mehr with the point clouds of Sha. One would have been motivated to combine these references because both references disclose 3D modeling and imaging, including with additive manufacturing/3D printing. Moreover, Sha enhances the printing of Mehr by “improv[ing] the speed at which image data from multiple sources is processed and aligned, which can improve performance and reduce processing hardware requirements for achieving desired performance benchmarks…” while also “improv[ing] the overall accuracy and feature density of the system as 3D images of the subject are captured” (See Sha paras. [0057] and [0078]).
As per claim 8, Mehr/Sha teaches the method as claimed in claim 7. However, while Mehr teaches points in a coordinate system, Mehr does not teach adjacent points to determine anomalies.
Sha further teaches including data of a working area from adjacent points to determine the anomaly value (See Sha paras. [0122-126] and [0178]: point cloud adjacency, while also using “points in the point cloud to determine an amount of error in between the features in the point cloud”. This would apply to the defect detection in Mehr).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Mehr with the teachings of Sha for at least the same reasons as discussed above in claim 7.
Claims 9 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mehr/Sha as applied above, and further in view of Severson et al. (U.S. Publication No. 2022/0114307; hereinafter “Severson”).
As per claim 9, Mehr/Sha teaches the method as claimed in claim 8. However, while Mehr/Sha teaches monitoring the liquid phase/melt pool (See Mehr paras. [0118-119]), Mehr/Sha does not teach or suggest matching the liquid phase and a spatial extent.
Severson teaches wherein the working area has a spatial extent matching a liquid phase prevailing at the observation time point (See Severson para. [0099]: “adjust the scaling factor C so that the temperature at the periphery of the 1.5-mm (dia.) melt pool matches the liquidus temperature (1830° C.).” Therefore, the melt pool dimensions of Mehr/Sha can be matched using the scaling factor and temperatures of Severson).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the digital twin and defect detection of Mehr/Sha with the double ellipsoid of Severson. One would have been motivated to combine these references because both references modeling in additive manufacturing, and Severson enhances the models of Mehr/Sha by improving the efficiency and accuracy of the heat distribution of the additive manufacturing of Mehr/Sha (See Severson paras. [0008-09]).
As per claim 11, Mehr/Sha teaches the method as claimed in claim 7. However, Mehr/Sha does not teach or suggest where the working area is represented by a double ellipsoid.
Severson teaches where the working area is represented by a double ellipsoid (See Severson paras. [0045] and [0091-93]: double ellipsoid modeling for the manufacturing of Mehr).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Mehr/Sha with the teachings of Severson for at least the same reasons as discussed above in claim 9.
Claim 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mehr as applied above, and further in view of Oh et al. (U.S. Publication No. 2022/0417557; hereinafter “Oh”).
As per claim 10, Mehr teaches the method as claimed in claim 1. However, Mehr does not teach a point cloud.
Oh teaches wherein the point cloud is represented in a spatially structured data structure in the form of an octree (See Oh paras. [0369] and [0380]: octree structure with the point cloud).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the digital twin and defect detection of Mehr with the point clouds of Oh. One would have been motivated to combine these references because both references disclose 3D modeling and imaging, and Oh enhances the models of Mehr by increasing their processing efficiency as there can be a large amount of point data (See Oh paras. [0002-03]).
Claims 12 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mehr as applied above, and further in view of MacNeish, III et al. (U.S. Publication No. 2019/0099952; hereinafter, “MacNeish”).
As per claim 12, Mehr teaches the method as claimed in claim 1. However, while Mehr teaches adjusting parameters as they relate to heat flux and other temperatures (See Mehr paras. [0108] and [0124]), Mehr does not explicitly teach higher heat input.
MacNeish teaches wherein adjustment of the process parameters for the elimination of the anomaly includes a higher heat input (See MacNeish paras. [0038], [0042], and [0044]: “a difference in temperate at the hot end 106 from a desired set point may represent a necessary adjustment by the PID controller of the thermocouple connectively associated with the heating element 303 of the hot end 106.” This includes increasing “the delivery of power to the heating element 303 of the hot end 106”).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the additive manufacturing corrective actions of Mehr with the corrective action adjustments of MacNeish. One would have been motivated to combine these references because both references disclose corrective actions in additive manufacturing, and MacNeish enhances the manufacturing of Mehr by increasing the flexibility and options of the control actions of Mehr. This would further decrease significant or fatal print flaws, as well as their frequency of occurrence of these printing breakdowns, while minimizing the number of settings needed to engage in the additive manufacturing (See MacNeish para. [0006]).
As per claim 13, Mehr teaches the method as claimed in claim 1. However, while Mehr teaches adjusting parameters as they relate to speed (See Mehr paras. [0052] and [0067]), Mehr does not explicitly teach higher heat input.
MacNeish further teaches wherein adjustment of the process parameters for the elimination of the anomaly includes a lower printhead speed (See MacNeish paras. [0044] and [0049-50]: “the controller 310 may indicate an increase or decrease in the print head hob speed…” Additionally, “[a] motor having encoding 1004 may be provided so that filament pull, grabbing, jamming, or crimping may be sensed to allow for ultimate adjustment of motor speed”).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Mehr with the teachings of MacNeish for at least the same reasons as discussed above in claim 12.
Claim 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mehr as applied above, and further in view of Nilakantan (U.S. Publication No. 2021/0178697).
As per claim 14, Mehr teaches the method as claimed in claim 1. However, while Mehr teaches wire additive manufacturing and arc welding (See Mehr paras. [0096]), Mehr does not explicitly teach wire arc additive manufacturing.
Nilakantan teaches the additive manufacturing method comprises wire arc additive manufacturing (See Nilakantan para. [0002]: additive manufacturing technology can include wire arc additive manufacturing).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the wire additive manufacturing of Mehr with the wire arc manufacturing of Nilakantan. One would have been motivated to combine these references because both references disclose modeling in wire additive manufacturing, and Nilakantan enhances the manufacturing of Mehr by increasing the flexibility by expanding the types of environments that the modeling of Mehr can apply to.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Davis et al. (U.S. Publication No. 2020/0394618) discloses digital twins employed in an additive manufacturing environment, as well as updating the models for different manufacturing variances. Surrogate models form digital twin of a specified/selected part.
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/NICHOLAS KLICOS/Primary Examiner, Art Unit 2118