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 .
Response to Arguments
Applicant’s arguments filed 01/04/26 & 08/11/25, with respect to the rejection claims 1-21 are rejected under the 35 U.S.C. 112(b), second paragraph & the 35 U.S.C. 103 have been fully considered and are persuasive, see for example pages 1-3. Therefore, the rejections of claims 1-21 have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made Richardson et al (US 2019/0389130) in view of Mehr et al (US 2018/0341248).
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 7-8, 12-13, and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Richardson et al (US 2019/0389130 hereinafter “Richardson”) in view of Mehr et al (US 2018/0341248 hereinafter “Mehr”).
Regarding claim 1; Richardson discloses an additive manufacturing system and a computer-implement method (additive layer manufacturing apparatus 10 @ figure 1) comprising:
an energy source (20 @ figure 1 and paragraph [0035]: e.g., The heat for melting the powder material 15 to induce fusion can be supplied by, in particular, a laser beam or a charged particle beam, in the case of this apparatus by an electron beam 18 generated and transmitted by an electron beam column 19 mounted on the housing 11. The column 19 comprises a controllable electron beam generating unit 20 containing an electron source) arranged to fuse metallic powder (14, 15 @ figure 1) disposed on a build plane (13 @ figure 1);
a sensor (optical image camera 24 @ figure 1) arranged to detect electromagnetic energy emitted during the fusing of the metallic powder (14, 15 @ figure 1and paragraph [0034]: e.g., The powder material 15 of the layer 15a is then selectively melted and fused by the action of heat in a predefined area. The powder material 15 in that area forms, after solidification, a cross-sectional layer of the article 14 perpendicular to the plane of the drawing); and
a processor (analysing unit [25 @ figure 1] coupled to first processor and second processor [26, 27 @ figure 1]) that receives data from the sensor (24 @ figure 1);
convert the data to a thermal energy (paragraph [0024]: e.g., the influencing means preferably comprises processing and controlling means for processing data supplied by the analysing means and indicative of recognized defects in a powder material layer and for controlling the powder deposition means in dependence on the processed data. The output of the analysing means or unit 25 can thus be converted into directly usable control data appropriate to requirements),
determine a planar area (15a @ figure 1) of the fused region (15 @ figure 1 and paragraph [0040]: e.g., Recognition of defects of these kinds is achieved by analysis of each imaged fringe pattern by an analysing unit 25 employing analysis techniques as described below in connection with FIG. 2. The analysing unit 25 produces first data which characterise, faults in the powder material layer 15a prior to beam action and second data which characterise faults in the article cross-sectional layer 14a evident after beam action, the first data being supplied to and processed by a first processor 26 for determining corrective action in relation to the powder disposition in the powder material layer 15a and the second data being supplied to and processed by a second processor 27 for determining corrective action in relation to the operation of the beam 18) on build plane (13 @ figure 1). See figures 1-2
Richardson discloses all of feature of claimed invention except for the processor for employing a trained machine learning algorithm model to detect a defect in the region based at least in part of the area on the thermal energy and the planar area of the fused region. However, Mehr teaches that it is known in the art to provide the processor (figure 12 and paragraph [0021]: e.g., a processor for running a machine learning algorithm that utilizes data from the machine vision and/or process monitoring tools, the process simulation tools, the post-build inspection tools, or any combination thereof, to provide real-time adaptive control of the deposition process) employs a trained machine learning algorithm model (paragraphs [0138]) to detect a defect (paragraphs [0021]-[0022] and [0165]: e.g., one or more processors may be employed to implement the machine learning algorithms, automated object defect classification methods, and additive manufacturing process control methods disclosed herein) in the region based at least in part on the thermal energy and the planar area of the fused region (figures 2 and 4A-4C and para. [0007] and [01 17]: e.g., The newly deposited layer forms a metallurgical bond with the substrate (or previously deposited layers) in a region referred to as the fusion zone. The propagation of heat through the newly deposited layer to the substrate (or previously deposited layers) may in some instances affect material properties within a region referred to as the heat affected zone. The solidification process may also cause metallurgical defects such as pores and cracks to form in the deposited layer. The quantity and type of defects that arise are dependent on the amount of heat input, the time spent at elevated temperatures, the geometry of the printed part, and the presence of contaminants near the melt pool).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filling date of claimed invention to combine additive manufacturing system of Richardson with the processor for employing a trained machine learning algorithm model to detect a defect in the region based at least in part of the area on the thermal energy and the planar area of the fused region as taught by Mehr for the purpose of improving the quality of the parts that are produced automated object defect classification and adaptive, real-time control of additive manufacturing and/or welding processes.
Regarding claim 15; Richardson discloses a computer-implement method (additive layer manufacturing apparatus 10 @ figure 1) comprising:
steering an energy source (20 @ figure 1 and paragraph [0035]: e.g., The heat for melting the powder material 15 to induce fusion can be supplied by, in particular, a laser beam or a charged particle beam, in the case of this apparatus by an electron beam 18 generated and transmitted by an electron beam column 19 mounted on the housing 11. The column 19 comprises a controllable electron beam generating unit 20 containing an electron source) arranged to fuse metallic powder (14, 15 @ figure 1) disposed on a build plane (13 @ figure 1);
detecting, a sensor (optical image camera 24 @ figure 1), electromagnetic energy emitted during the fusing of the metallic powder (14, 15 @ figure 1and paragraph [0034]: e.g., The powder material 15 of the layer 15a is then selectively melted and fused by the action of heat in a predefined area…paragraph [0035]: e.g., The heat for melting the powder material 15 to induce fusion can be supplied by, in particular, a laser beam or a charged particle beam, in the case of this apparatus by an electron beam 18 generated and transmitted by an electron beam column 19 mounted on the housing 11). See figures 1-2
Richardson discloses all of feature of claimed invention except for detecting, by a trained machine learning algorithm model, a defect in the fused of metallic powder based at least in part on a thermal energy and a planar area of the fused region. However, Mehr teaches that it is known in the art to provide the processor (figure 12 and paragraph [0021]: e.g., a processor for running a machine learning algorithm that utilizes data from the machine vision and/or process monitoring tools, the process simulation tools, the post-build inspection tools, or any combination thereof, to provide real-time adaptive control of the deposition process), by a trained machine learning algorithm model (paragraphs [0138]) to detect a defect (paragraphs [0021]-[0022] and [0165]: e.g., one or more processors may be employed to implement the machine learning algorithms, automated object defect classification methods, and additive manufacturing process control methods disclosed herein) in the fused of metallic powder based at least in part on a thermal energy and a planar area of the fused region
(figures 2 and 4A-4C and para. [0007] and [01 17]: e.g., The newly deposited layer forms a metallurgical bond with the substrate (or previously deposited layers) in a region referred to as the fusion zone. The propagation of heat through the newly deposited layer to the substrate (or previously deposited layers) may in some instances affect material properties within a region referred to as the heat affected zone. The solidification process may also cause metallurgical defects such as pores and cracks to form in the deposited layer. The quantity and type of defects that arise are dependent on the amount of heat input, the time spent at elevated temperatures, the geometry of the printed part, and the presence of contaminants near the melt pool).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filling date of claimed invention to combine additive manufacturing system of Richardson with detecting, by a trained machine learning algorithm model, a defect in the fused of metallic powder based at least in part on a thermal energy and a planar area of the fused region as taught by Mehr for the purpose of improving the quality of the parts that are produced automated object defect classification and adaptive, real-time control of additive manufacturing and/or welding processes.
Regarding claims 2 and 16; Richardson discloses all of feature of claimed invention except for the processor is further configured to perform the operations to determine that a defect is repairable comprising determining by the trained machine learning algorithm determines a type of the defect. Mehr teaches that it is known in the art to provide the operations to determine that a defect is repairable (paragraphs [0124] and [0137]) comprising determining by the trained machine learning algorithm determines a type of the defect (paragraph [0006]: e.g., a processor programmed to provide a classification of detected object defects using a machine learning algorithm that has been trained using the training data set of step (a), wherein 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). It would have been obvious to one having ordinary skill in the art before the effective filling date of claimed invention to combine additive manufacturing system of Richardson with limitation above by Mehr for the purpose of improving the quality of the parts that are produced automated object defect classification and adaptive, real-time control of additive manufacturing and/or welding processes.
Regarding claims 3 and 8; Richardson discloses all of feature of claimed invention except for the type of the one or more defects includes at least one of a lack of fusion defect, a porosity defect or an inclusion defect of the metallic powder. However, Mehr teaches that it is known in the art to provide the type of the one or more defects includes at least one of a lack of fusion defect (paragraph [0071]: e.g., lack of fusion (LOF) defects. Note that LOF defects may occur even at low wire feed rates for which the resulting beads are more or less indistinguishable from normal bead depositions), a porosity defect or an inclusion defect of the metallic powder (figures 2 and 4A-4C, and paragraph [0049]: e.g., high power laser is used to melt and fuse metallic powders. A partis built by selectively melting and fusing powders within and between layers). It would have been obvious to one having ordinary skill in the art before the effective of filling date of claimed invention to combine additive manufacturing system of Richardson with limitation above as taught by Mehr for the purpose of improving the accuracy of the fabricated part and the overall build efficiency.
It is noted that the term "or" is alternative.
Regarding claim 7; Richardson discloses all of feature of claimed invention except for the trained machine learning algorithm includes one or more training parameters based on a known-defective part. However, Mehr teaches that it is known in the art to provide the trained machine learning algorithm includes one or more training parameters based on a known-defective part (paragraph [0007]: e.g., machine learning algorithm and a training data set that comprises object property data for defective and defect-free objects). It would have been obvious to one having ordinary skill in the art before the effective of filling date of claimed invention to combine additive manufacturing system of Richardson with limitation above as taught by Mehr for the purpose of improving the accuracy of the fabricated part and the overall build efficiency.
Regarding claim 12; Richardson discloses all of feature of claimed invention except for the processor is configured to halt the fusing of the metallic powder in response to a determination that the defect in the fused metal powder cannot be repairable. However, Mehr teaches that it is known in the art to provide the processor (figures 12-13) is configured to halt the fusing of the metallic powder in response to a determination that the defect in the fused metal powder cannot be repairable (paragraph [0031]: e.g., in-process inspection data (e.g., automated defect classification data) may be used by the machine learning algorithm to send a warning or error signal to an operator, or optionally, to automatically abort the deposition process, e.g., an additive manufacturing process). It would have been obvious to one having ordinary skill in the art before the effective of filling date of claimed invention to combine additive manufacturing system of Richardson with limitation above as taught by Mehr for the purpose of improving the accuracy of the fabricated part and the overall build efficiency.
Regarding claim 13; Richardson discloses all of feature of claimed invention except for the processor is configured to perform a remedy algorithm in response to a determination that the defect in the fused metal powder can be repairable. However, Mehr teaches that it is known in the art to provide the processor (figures 12-13) is configured to perform a remedy algorithm (figures 10-11 and 14) in response to a determination that the defect in the fused metal powder can be repairable (paragraph [0124] and [0137]). It would have been obvious to one having ordinary skill in the art before the effective of filling date of claimed invention to combine additive manufacturing system of Richardson with limitation above as taught by Mehr for the purpose of improving the accuracy of the fabricated part and the overall build efficiency.
Claims 4-6, 10-11, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Richardson in view of Mehr as applied to claim 1 and 15 above, and further in view of Becket et al (US 2019/0255654 hereinafter “Becket”).
Regarding claims 4 and 18; Richardson discloses all of feature of claimed invention except for the processor is configured to calculate one or more parameters based at least in part on the electromagnetic energy of the energy source. However, Becket teaches that it is known in the art to provide the processor (116 @ figure 1) is configured to calculate one or more parameters (paragraphs [0006] and [0034]: e.g., an additive manufacturing system that uses one or more optical sensing apparatus to determine the thermal energy density. The thermal energy density is sensitive to changes in process parameters such as, for example, energy source power, energy source speed, and hatch spacing) based at least in part on the electromagnetic energy of the energy source (100 @ figure 1). It would have been obvious to one having ordinary skill in the art before the effective of filling date of claimed invention to combine additive manufacturing system of Richardson with limitation above as taught by Beckett for the purpose determining accurately verifying the mechanical, geometrical and metallurgical properties of a production part produced by additive manufacturing.
Regarding claim 5 and 19; Richardson in view of Mehr combination discloses the one or more parameters includes determining a thermal emission density (TED) that includes measuring an amount of energy radiated from the fused metallic powder during one or more scans of the energy source and determining area of a build plane traversed during the one or more scans. However, Beckett teaches that it is known in the art to provide the one or more parameters includes determining a thermal emission density TED (paragraph [0141]: e.g., the processor 2018 can be used to perform calculations using the data collected by the various sensors to generate in-process quality metrics. In some embodiments, data generated by on-axis optical sensors 2009 can be used to determine thermal energy density TED during the build process…the thermal density and/or other metrics can be used by processor 2018 to generate control signals for process parameters, for example, laser power, laser speed, hatch spacing, and other process parameters in response to the thermal energy density or other metrics falling outside of desired ranges) that includes measuring an amount of energy radiated from the fused metallic powder (paragraph [0139]: e.g., there could be contact sensors on a recoater arm configured to spread metallic powders across build plane 2005. These sensors could be accelerometers, vibration sensors, etc. Lastly, there could be other types of sensors such as thermocouples to measure macro thermal fields or could include acoustic emission sensors which could detect cracking and other metallurgical phenomena occurring in the deposit as it is being built) during one or more scans of the energy source (2000 @ figure 20B) and determining area of a build plane traversed during the one or more scans (paragraph 0141]). It would have been obvious to one having ordinary skill in the art before the effective of filling date of claimed invention to combine additive manufacturing system of Richardson with limitation above as taught by Beckett for the purpose determining accurately verifying the mechanical, geometrical and metallurgical properties of a production part produced by additive manufacturing.
Regarding claims 6 and 20; Richardson in view of Mehr combination discloses all of feature of claimed invention except for the one or more parameters includes identifying spectral peaks associated with material properties of a batch of powder and selecting a first wavelength and a second wavelength spaced apart from the first wavelength, and determining an amount of energy radiated from the build plane based upon a ratio of energy radiated at the first wavelength to energy radiated at the second wavelength. However, Beckett teaches that it is known in the art to provide the one or more parameters includes identifying spectral peaks associated with material properties of a batch of powder and selecting a first wavelength and a second wavelength spaced apart from the first wavelength, and determining an amount of energy radiated from the build plane based upon a ratio of energy radiated at the first wavelength to energy radiated at the second wavelength (claim 1 and paragraphs [0008] - [0009]:e.g., identifying spectral peaks associated with a batch of powder; selecting a first wavelength and a second wavelength spaced apart from the first wavelength, the first and second wavelengths being offset from the identified spectral peaks; generating a plurality of scans of an energy source across a layer of the batch of powder on a build plane; generating sensor readings during each of the plurality of scans using an optical sensing system that monitors the first wavelength and the second wavelength; determining variations in temperature across the build plane during the plurality of scans using a ratio of the sensor readings collected at the first wavelength to the sensor readings collected at the second wavelength). It would have been obvious to one having ordinary skill in the art before the effective of filling date of claimed invention to combine additive manufacturing system of Richardson with limitation above as taught by Beckett for the purpose determining accurately verifying the mechanical, geometrical and metallurgical properties of a production part produced by additive manufacturing.
Regarding claims 10 and 17; Richardson in view of Mehr combination discloses all of feature of claimed invention except for the trained machine learning algorithm detects the defects based at least in part on a ratio of the thermal energy to the planar area of the fused region. However, Beckett teaches that it is known in the art to provide the trained machine learning algorithm (paragraph [0060]: e.g., the mathematical manipulation of data to linearly or non-linearly map the raw data into another variable space of lower dimensionality using a transformation law or algorithm) of the processor (116 @ figure 1) detects the defects (paragraph [0061]: e.g., In regions of the part where a difference between the calculated TED and baseline data set exceeds a threshold value, those regions can be identified as possibly including one or more defects and/or further processing can be performed on the region in near real-time to ameliorate any defects caused by the variation of TED from the baseline data set. In some embodiments, the portions of the part that may contain defects can be identified using a classifier) based at least in part on a ratio of the thermal energy to the planar area of the fused region (Table I and paragraph [0130]: e.g., A ratio of the intensity of light at wavelength 1854 to the intensity of light at wavelength 1856 can be used to characterize changes or variations in temperature on the build plane. These measurements are driven by thermal radiation from the melt pool and a luminous plume proximate the melt pool that is caused by vaporization of small portions of the metal powder). It would have been obvious to one having ordinary skill in the art before the effective of filling date of claimed invention to combine additive manufacturing system of Richardson with limitation above as taught by Beckett for the purpose determining accurately verifying the mechanical, geometrical and metallurgical properties of a production part produced by additive manufacturing.
Regarding claim 11; Richardson in view of Mehr combination discloses all of feature of claimed invention except for the one or more sensors includes an on-axis photodetector. However, Beckett teaches that it is known in the art to provide the one or more sensors (paragraph [0044]: e.g., the optical signal 107 may be interrogated by multiple on-axis optical sensors 109 each receiving a portion of the optical signal 107 through a series of additional partially reflective mirrors 108) includes an on-axis photodetector (109 @ figure 1). It would have been obvious to one having ordinary skill in the art before the effective of filling date of claimed invention to combine additive manufacturing system of Richardson with limitation above as taught by Beckett for the purpose determining accurately verifying the mechanical, geometrical and metallurgical properties of a production part produced by additive manufacturing.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Richardson in view of Mehr as applied to claim 7 above, and further in view of DehghanNiri et al (US 2018/0126670 hereinafter “DehghanNiri”).
Regarding claim 9; Richardson in view of Mehr combination discloses all of feature of claimed invention except for the one or more training parameters are derived from a known-defective part having at least one inclusion. However, DehghanNiri et al teaches that it is known in the art to provide the one or more training parameters are derived from a known-defective part having at least one inclusion (paragraph [0027]: e.g., a 2mm void and a 2 mm inclusion can be detected in the calibration block 170, so that their response may be used for classifying these kinds of defects). It would have been obvious to one having ordinary skill in the art before the effective of filling date of claimed invention to combine additive manufacturing system of Richardson with limitation above as taught by DehghanNiri for the purpose improving classification these defects.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Richardson in view of Mehr as applied to claim 13 above, and further in view of Redding et al (US 2017/0146489 hereinafter “Redding”).
Regarding claim 14; Richardson in view of Mehr combination discloses all of feature of claimed invention except for the remedy algorithm comprises: determining a location corresponding to the defect; and steering a beam of energy to the location to correct the defect, the beam of energy generated by the energy source. However, Redding teaches that it is known in the art to provide the remedy algorithm (10 @ figure 1 and paragraph [0042]) comprises: determining a location corresponding to the defect (figure 4 and paragraph [0037]: e.g.., a fourth group 84 of cells corresponds to the location of the defect 66); and steering a beam of energy (B @ figure 1) to the location to correct the defect of the workpiece (W @ figure 1), the beam of energy (B @ figure 1) generated by the energy source (24 @ figure 1). It would have been obvious to one having ordinary skill in the art before the effective of filling date of claimed invention to combine additive manufacturing system of Richardson with limitation above as taught by Redding for the purpose of improving inspecting an additive manufacturing process in which a directed energy source is used to create a weld pool at an exposed build surface of a mass of powdered material.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Richardson et al (US 2019/0389130 hereinafter “Richardson”) in view of Mehr et al (US 2018/0341248 hereinafter “Mehr”) and Beckett et al (US 2019/0255654 hereinafter “Beckett”).
Regarding claim 21; Richardson discloses an additive manufacturing system and a computer-implement method (additive layer manufacturing apparatus 10 @ figure 1) comprising:
an energy source (20 @ figure 1 and paragraph [0035]: e.g., The heat for melting the powder material 15 to induce fusion can be supplied by, in particular, a laser beam or a charged particle beam, in the case of this apparatus by an electron beam 18 generated and transmitted by an electron beam column 19 mounted on the housing 11. The column 19 comprises a controllable electron beam generating unit 20 containing an electron source) arranged to fuse metallic powder (14, 15 @ figure 1) disposed on a build plane (13 @ figure 1);
a sensor (optical image camera 24 @ figure 1) arranged to detect electromagnetic energy emitted during the fusing of the metallic powder (14, 15 @ figure 1and paragraph [0034]: e.g., The powder material 15 of the layer 15a is then selectively melted and fused by the action of heat in a predefined area. The powder material 15 in that area forms, after solidification, a cross-sectional layer of the article 14 perpendicular to the plane of the drawing); and
a processor (analysing unit [25 @ figure 1] coupled to first processor and second processor [26, 27 @ figure 1]) that receives data from the sensor (24 @ figure 1);
convert the data to a thermal energy (paragraph [0024]: e.g., the influencing means preferably comprises processing and controlling means for processing data supplied by the analysing means and indicative of recognized defects in a powder material layer and for controlling the powder deposition means in dependence on the processed data. The output of the analysing means or unit 25 can thus be converted into directly usable control data appropriate to requirements),
determine a planar area (15a @ figure 1) of the fused region (15 @ figure 1 and paragraph [0040]: e.g., Recognition of defects of these kinds is achieved by analysis of each imaged fringe pattern by an analysing unit 25 employing analysis techniques as described below in connection with FIG. 2. The analysing unit 25 produces first data which characterise, faults in the powder material layer 15a prior to beam action and second data which characterise faults in the article cross-sectional layer 14a evident after beam action, the first data being supplied to and processed by a first processor 26 for determining corrective action in relation to the powder disposition in the powder material layer 15a and the second data being supplied to and processed by a second processor 27 for determining corrective action in relation to the operation of the beam 18) on build plane (13 @ figure 1).
Richardson discloses all of feature of claimed invention except for the processor for employing a trained machine learning algorithm model to detect a defect in the region based at least in part of the area on the thermal energy and the planar area of the fused region. However, Mehr teaches that it is known in the art to provide the processor (figure 12 and paragraph [0021]: e.g., a processor for running a machine learning algorithm that utilizes data from the machine vision and/or process monitoring tools, the process simulation tools, the post-build inspection tools, or any combination thereof, to provide real-time adaptive control of the deposition process) employs a trained machine learning algorithm model (paragraphs [0138]) to detect a defect (paragraphs [0021]-[0022] and [0165]: e.g., one or more processors may be employed to implement the machine learning algorithms, automated object defect classification methods, and additive manufacturing process control methods disclosed herein) in the region based at least in part on the thermal energy and the planar area of the fused region (figures 2 and 4A- 4C and para. [0007] and [01 17]: e.g., The newly deposited layer forms a metallurgical bond with the substrate (or previously deposited layers) in a region referred to as the fusion zone. The propagation of heat through the newly deposited layer to the substrate (or previously deposited layers) may in some instances affect material properties within a region referred to as the heat affected zone. The solidification process may also cause metallurgical defects such as pores and cracks to form in the deposited layer. The quantity and type of defects that arise are dependent on the amount of heat input, the time spent at elevated temperatures, the geometry of the printed part, and the presence of contaminants near the melt pool). It would have been obvious to one having ordinary skill in the art before the effective filling date of claimed invention to combine additive manufacturing system of Richardson with the processor for employing for example, a processor executing instructions stored on a computer readable medium that trained machine learning algorithm model to detect a defect in the region based at least in part of the area on the thermal energy and the planar area of the fused region as taught by Mehr for the purpose of improving the quality of the parts that are produced automated object defect classification and adaptive, real-time control of additive manufacturing and/or welding processes.
Richardson in view of Mehr combination discloses all of feature of claimed invention except for the sensor configured to detect first and second bands of wavelengths emitted during the fusing of the metallic powder, wherein each of the first band of wavelengths and the second band of wavelengths are spaced apart from one or more characteristic spectral peaks associated with the metallic powder. However, Beckett et al teaches that it is known in the art to provide the sensor (2009-1, 2009-2 @ figures 20A-20B) configured to detect first and second bands of wavelengths emitted during the fusing of the metallic powder (2005 @ figures 20A-20B and paragraphs [0008]-[0009], [0139], and claim 1), wherein each of the first band of wavelengths and the second band of wavelengths are spaced apart from one or more characteristic spectral peaks associated with the metallic powder (claim 1 and paragraphs [0008] - [0009]: e.g., identifying spectral peaks associated with a batch of powder; selecting a first wavelength and a second wavelength spaced apart from the first wavelength, the first and second wavelengths being offset from the identified spectral peaks; generating a plurality of scans of an energy source across a layer of the batch of powder on a build plane; generating sensor readings during each of the plurality of scans using an optical sensing system that monitors the first wavelength and the second wavelength). It would have been obvious to one having ordinary skill in the art before the effective of filling date of claimed invention to combine additive manufacturing system of Richardson with limitation above as taught by Beckett for the purpose determining accurately verifying the mechanical, geometrical and metallurgical properties of a production part produced by additive manufacturing.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Hall (US 2018/0154484) discloses an additive manufacturing apparatus and corresponding method for building an object by layerwise consolidation of material.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANG H NGUYEN whose telephone number is (571)272-2425. The examiner can normally be reached M-F.
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April 20, 2026
/SANG H NGUYEN/ Primary Examiner, Art Unit 2877
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