DETAILED ACTION
Claims 1-12 and 14-21 are currently pending.
The previous rejections to claims 1-12 and 14-21 under 35 U.S.C. 101 are withdrawn due to Applicant’s amendment.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/6/26 has been entered.
Response to Arguments
Applicant’s arguments with respect to claims 1-12 and 14-21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1-12 and 14-21 are rejected under 35 U.S.C. 103 as being unpatentable over Kawanaka et al. US Publication 2025/0050586 (hereafter “Kawanaka”) and Scime et al. US Publication 2022/0134435 (hereafter “Scime”).
Referring to claims 1, 12, and 20, Kawanaka discloses a method comprising:
obtaining an image representing a volume of molten material generated by an additive manufacturing process of a part (paragraph 79, the additive manufacturing condition search device 2 of the present embodiment performs shape measurement during modeling using intensity data of a specific wavelength and image data by an optical camera), wherein the volume of molten material is distinguished from the part as a whole by reaction with a laser (paragraph 54, Here, the light beam includes a laser beam and an electron beam, and further includes various beams capable of melting the metal powder);
extracting, using the image, (i) a first feature that indicates a characteristic of the volume of molten material generated by the additive manufacturing process of the part (ii) a second feature that indicates a temperature of the volume of molten material (paragraph 42, The temperature sensor 56 includes a contact type temperature sensor such as a thermocouple that measures a temperature of the stage 518, and a non-contact type temperature sensor such as an infrared radiation thermometer that measures a temperature of the powder bed formed on the stage 518);
providing the first feature and the second feature to a trained machine learning model (paragraph 103, Operation of the DSCNN generally includes receiving an input layer 330);
obtaining output from the trained machine learning model processing the first feature and the second feature (paragraph 129, The additive manufacturing apparatus 5 acquires monitoring information during additive manufacturing while additively manufacturing the modeled object by using this recipe (step S33);
determining, using the output of the trained machine learning model processing the first feature and the second feature, a predicted porosity of the part (paragraph 120, A vertical axis represents the density of the additively manufactured product. The higher the density of the additively manufactured product, the lower the defect rate. It is necessary to set the additive manufacturing conditions at an appropriate energy density such that the density of the additively manufactured product exceeds a predetermined value for each of materials A to C); and
adjusting, based on the predicted porosity of the part, manufacturing of the part to mitigate the predicted porosity of the part (paragraph 132, Accordingly, the first machine learning unit 47 can correct the recommended recipe until the score reaches the evaluation target value).
While Kawanaka discloses extracting a first feature, Kawanaka does not disclose expressly that the first feature is a characteristic of ejecta projected from the volume of molten material.
Scime discloses extracting, using the image, (i) a first feature that indicates a characteristic of ejecta projected from the volume of molten material generated by the additive manufacturing process of the part (paragraph 28, The software platform can monitor essentially any powder bed additive manufacturing process and provide segmentation and classification of anomalies it is trained to detect. A few examples of anomalies capable of being detected by some embodiments include melt ejecta) and (ii) a second feature (paragraph 28, The software platform can monitor essentially any powder bed additive manufacturing process and provide segmentation and classification of anomalies it is trained to detect. A few examples of anomalies capable of being detected by some embodiments include melt ejecta);
providing the first feature and the second feature to a trained machine learning model (paragraph 103, Operation of the DSCNN generally includes receiving an input layer 330);
obtaining output from the trained machine learning model processing the first feature and the second feature (paragraph 105, The results of the classification can be returned to the software platform for presentation to the user, e.g., in raw form, in a visualized form, or in some other form 348);
determining, using the output of the trained machine learning model processing the first feature and the second feature, a predicted porosity of the part (paragraph 59-60, the systems and methods of the present disclosure can classify pixel-wise images to differentiate between various types of anomalies produced by powder bed manufacturing processes. Porosity, e.g., lack-of-fusion porosity in the EB-PBF process, generally encompasses a range of small-scale defects. For example, some porosity can be surface-visible because of geometry-induced change in emissivity in the near infrared (“NIR”) wavelength range); and
adjusting, based on the predicted porosity of the part, manufacturing of the part to mitigate the predicted porosity of the part (paragraph 42, these correlations can be utilized to identify the root cause of an anomaly and also can be helpful in providing a tool for changing process controls to prevent or reduce anomaly generation during particular additive manufacturing situations in the future).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to extract a characteristic of ejecta projected from the volume of molten material. The motivation for doing so would have been to increase the accuracy of detecting defects in additive manufacturing. Therefore, it would have been obvious to combine Scime with Kawanaka to obtain the invention as specified in claims 1, 12, and 20.
Referring to claim 2, Kawanaka discloses wherein determining the predicted porosity of the part comprises using the output of the trained machine learning model comprises:
determining, for a future point in time, a severity of porosity of the part (paragraph 139, The additive manufacturing condition search device 2 causes the generation unit 42 to generate the prediction model for predicting the input parameter that is the solution that satisfies the target value of the modeling result of the standard sample 1 (step S13). Specifically, the additive manufacturing condition search device 2 generates, as the prediction model, a function indicating a relationship between pieces of input and output data of the additive manufacturing apparatus 5 by using data (for example, initial data) stored in the storage unit 22).
Referring to claims 3 and 14, Kawanaka discloses wherein determining the predicted porosity of the part comprises using the output of the trained machine learning model comprises:
determining, for a future point in time, a type of defect (paragraph 124, The data processing unit 67 performs machine learning by using, as train data, past monitoring information of the monitoring information database 661 and a past defect determination result of the defect determination result database 662, and creates a model that predicts the defect determination result in a case where the monitoring information is used as an input).
Referring to claims 4 and 15, Kawanaka discloses wherein the type of defect is lack of fusion, conduction, or keyholing (paragraph 67, When powder is irradiated with a beam, in a case where there is no material to be bonded to the melted powder and in a case where heat cannot be dissipated quickly due to heat conduction, the melted powder shrinks into a spherical shape to form a relatively large spherical lump on the powder. Thus, in the “Down-skin” that forms the overhang, a condition for suppressing energy is selected).
Referring to claims 5 and 16, Kawanaka discloses providing feedback to the additive manufacturing process, wherein the feedback is configured to adjust the manufacturing of the part (paragraph 132, Accordingly, the first machine learning unit 47 can correct the recommended recipe until the score reaches the evaluation target value).
Referring to claims 6 and 17, Kawanaka discloses wherein adjusting the manufacturing of the part comprises adjusting one or more of a laser power, scanning speed, or delay (paragraph 72, Here, the control factors an output and a scanning speed of a light beam with which the contour line is irradiated).
Referring to claims 7 and 18, Kawanaka discloses wherein determining the predicted porosity of the part comprises determining that continuing the additive manufacturing process will cause a defect in the part without adjustments to the additive manufacturing process (paragraph 132, Accordingly, the first machine learning unit 47 can correct the recommended recipe until the score reaches the evaluation target value) (paragraph 67, When powder is irradiated with a beam, in a case where there is no material to be bonded to the melted powder and in a case where heat cannot be dissipated quickly due to heat conduction, the melted powder shrinks into a spherical shape to form a relatively large spherical lump on the powder. Thus, in the “Down-skin” that forms the overhang, a condition for suppressing energy is selected).
Referring to claims 8 and 19, Kawanaka discloses wherein at least one of the first feature or the second feature indicates one or more features include one or more of a value indicating a length of the volume, a value indicating a spread of ejecta, a value indicating a temperature of ejecta, or a value indicating a temperature of the volume (paragraph 42, The temperature sensor 56 includes a contact type temperature sensor such as a thermocouple that measures a temperature of the stage 518, and a non-contact type temperature sensor such as an infrared radiation thermometer that measures a temperature of the powder bed formed on the stage 518)
Referring to claim 9, Kawanaka discloses wherein at least one of the first feature or the second feature indicates a shape of the volume of molten material (paragraph 74, FIG. 8 illustrates measurement portions of a total height Z, a width X, and a width Y of the sample as a modeling result of a shape of the additively manufactured standard sample 1).
Referring to claim 10, Kawanaka discloses wherein the trained machine learning model includes one or more of a Logistic Regression (LR) model, a Support Vector Machine (SVM), or K-Nearest Neighbors (KNN) algorithm (paragraph 105, The generation unit 42 generates a prediction model indicating a relationship between the setting value of the condition within the search region and the actual measurement value of the output by statistical analysis such as regression analysis capable of coping with multiple-input and multiple-output such as a neural network and a support vector machine, correlation analysis, principal component analysis, or multiple regression analysis).
Referring to claim 11, Kawanaka discloses providing an indication that a second part has no defect (paragraph 132, In step S37, when the first machine learning unit 47 performs regression analysis of the score and the parameter of the modeling result to derive a new recommended recipe, the processing returns to step S33. Then, the additive manufacturing condition search device 2 repeats a series of processing from steps S33 to S36 based on the recommended recipe. Accordingly, the first machine learning unit 47 can correct the recommended recipe until the score reaches the evaluation target value).
Referring to claim 21, Kawanaka discloses extracting, using the image, (i) the first feature that indicates a characteristic generated by the additive manufacturing process, (ii) the second feature that indicates the temperature of the volume of molten material, and (iii) a third feature that indicates a shape of the volume of molten material (paragraph 74, FIG. 8 illustrates measurement portions of a total height Z, a width X, and a width Y of the sample as a modeling result of a shape of the additively manufactured standard sample 1); and
providing the first feature, the second feature, and the third feature, to the trained machine learning model to generate the output used to determine the predicted porosity of the part (paragraph 129, The additive manufacturing apparatus 5 acquires monitoring information during additive manufacturing while additively manufacturing the modeled object by using this recipe (step S33).
Scime discloses (i) a first feature that indicates a characteristic of ejecta projected from the volume of molten material generated by the additive manufacturing process (paragraph 28, The software platform can monitor essentially any powder bed additive manufacturing process and provide segmentation and classification of anomalies it is trained to detect. A few examples of anomalies capable of being detected by some embodiments include melt ejecta).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER K HUNTSINGER whose telephone number is (571)272-7435. The examiner can normally be reached Monday - Friday 8:30 - 5:00.
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/PETER K HUNTSINGER/Primary Examiner, Art Unit 2682
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