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 .
Claims 1-20 filed on 12/19/2024 have been reviewed and considered by this office action.
Information Disclosure Statement
The information disclosure statement filed on 03/06/2025 has been reviewed and considered by this office action.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: 406.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character “402” has been used to designate both operation “Deposition of Layer” and operation “Scan Layer”.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The specification filed on 12/19/2024 has been reviewed and is considered acceptable.
Claim Objections
Claim 4-5, 8 and 12 are objected to because of the following informalities:
Claim 4 recites the limitation “the step of instructing the robotic arm.” There is insufficient antecedent basis for this limitation in the claim. For the purpose of examination: the limitation will be interpreted to recite “a step of instructing the robotic arm.”
Claim 5 recites the limitation “the step of instructing the robotic arm.” There is insufficient antecedent basis for this limitation in the claim. For the purpose of examination: the limitation will be interpreted to recite “a step of instructing the robotic arm.”
In claim 8, “a three-dimensional object” is used twice. For the purpose of examination: the second use of “a three-dimensional object” will be treated as “the three-dimensional object.”
In claim 12, “a defect” and “a manufacturing defect” are both used. It is unclear whether “a defect” and “a manufacturing defect” refer to the same defect. For the purposes of examination “a manufacturing defect” will be treated as “a defect.”
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.
Claim(s) 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.
Regarding claim 9, the claim recites “the defect” which is indefinite. Prior to the recitation of “the defect”, “a defect” is recited twice and it is unclear which “a defect” is being referred to. For the purpose of examination: “The defect” will be interpreted as referring to the first occurrence of “a defect”.
Regarding claim 10, the claim recites “the defect” which is indefinite. Prior to the recitation of “the defect”, “a defect” is recited twice, it is unclear which “a defect” is being referred to. For the purpose of examination: “The defect” will be interpreted as referring to the first occurrence of “a defect”.
Regarding claim 10, the claim recites “overriding the step of repairing the defect” which is indefinite. Claim 10 requires both “repairing a defect”, from claim 9, and “overriding the step of repairing a defect” which requires that the defect be both repaired and that the repair does not occur. For the purpose of examination: “The method of claim 9, further comprising” will be treated as “the method of claim 8, further comprising”.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 10 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Regarding claim 10, the claim recites “overriding the step of repairing the defect” which does not further limit the subject matter claimed. Claim 10 requires both “repairing a defect”, from claim 9, and “overriding the step of repairing a defect” which eliminates the subject matter of claim 9. By negating the claim upon which it depends, claim 10 fails to specify a further limitation of the subject matter claimed.
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, 7-9, 12-15 and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Reese et al. (US 20160236414 A1).
Regarding claim 1, Reese discloses A system for identifying a defect in an additive manufacturing process, the system comprising: ([0008]: “The present invention provides an improved method of additive manufacturing comprising monitoring and identification of defects occurring in a 3D object while it is being printed;”).
a robotic arm configured for manufacturing a three-dimensional object ([0030] Furthermore, the tool attachments also have the ability to orient themselves with multiple degrees of freedom since they are attached to functional tool heads provided with multi-axis motion mechanism. For a person skilled in the art, it may be obvious that multi-axis motion mechanism, such as robotic arms or manipulators, are present in the art to provide attachment for a number of tool attachments and allow them to perform various functions.) by melting and solidifying ([0031]: “while the other tool attachments may perform other ancillary functionalities such as cooling, heating, deburring, milling etc.”).
a metal material via a heat source; ([0050]: “For the additive manufacturing process, the extruding head may be fed with a building material from which the object may be printed. In an embodiment, the building material may be an amorphous polymer, a semi-crystalline polymer, a metal, a ceramic, carbon or other reinforced material, or the like.”; [0031] “while the other tool attachments may perform other ancillary functionalities such as cooling, heating, deburring, milling etc.”).
one or more sensors configured for monitoring the metal material during manufacture of the three-dimensional object; ([0084]: “The quality detecting devices 400 may include and are not limited to thermal sensors, cameras, lasers, or any other available image detection devices, infrared devices to check the geometry of the object, audio microphones to capture abnormal extrusion sounds, which indicates if the filament is properly dried, and the like devices.”).
memory comprising instructions; at least one processor configured to execute the instructions to perform steps comprising: ([0031]: “The tool attachments of the build apparatus may function based on the tool path instructions 206, and are controlled by a controller.”).
receiving information from the one or more sensors during manufacture of the three-dimensional object; ([0084]: “The quality detecting devices 400 may include and are not limited to thermal sensors, cameras, lasers, or any other available image detection devices, infrared devices to check the geometry of the object, audio microphones to capture abnormal extrusion sounds, which indicates if the filament is properly dried, and the like devices.”).
receiving information regarding a microstructure of the metal material; ([0027]: “In an embodiment, the user device may contain programs such as AutoCad, wherein the user may prepare a CAD file defining the characteristics of the 3D object, including geometrical, mechanical, thermal, chemical, electrical and other property constraints of the object.”).
assigning a weight to the information from the one or more sensors based on one or more of the information received regarding the microstructure of the metal material, the operating characteristics of the one or more sensors, and the geometry of the three-dimensional object; and determining, using the weighted information, the likelihood of a defect in the material. ([0039]: “When the defects are identified by the quality detecting devices, the features of the defects are communicated to the controller, such as a defect feedback controller, where the defects are analyzed and processed.”; [0040] “Depending on the feature of defects, the system 200 stops building the object and generates a build report featuring the defects/errors. This may be the case when the errors are catastrophic (shown by 216).”; [0041] “On the other hand, when the errors are not catastrophic, the defect feedback controller may generate a set of correcting instructions for repairing the defects in the printable layer.”; [0072]: “The build report also compares the actual material properties of the final object compared to the user's inputted specifications and identifies any properties that fall outside the specifications, by implementing analyzing techniques.”; [0084]: “The quality detecting devices 400 may include and are not limited to thermal sensors, cameras, lasers, or any other available image detection devices, infrared devices to check the geometry of the object, audio microphones to capture abnormal extrusion sounds, which indicates if the filament is properly dried, and the like devices.”, where making a determination of whether the errors are catastrophic is a weight based on operational characteristics of sensors).
Regarding claim 2, Reese teaches the system of claim 1.
Reese further teaches wherein the one or more sensors are coupled with the robotic arm. ([0096]: “Robotic arm Y can employ a number of interchangeable fixtures to correct defects during part manufacturing. Considering, ‘B’ and ‘C’ defects are detected real-time using a multi-axis system consisting of D devices during build production, where D devices are quality detecting devices.”).
Regarding claim 3, Reese teaches the system of claim 1.
Reese further teaches wherein the one or more sensors includes one or more of an electromagnetic acoustic transducer, an air coupled transducer, a welding microphone, a laser interferometer, a laser profilometer, a non-contact probe, a visual camera, an infrared camera, an ultrasound probe, a position sensor, and a bead profiler. ([0084]: “The quality detecting devices 400 may include and are not limited to thermal sensors, cameras, lasers, or any other available image detection devices, infrared devices to check the geometry of the object, audio microphones to capture abnormal extrusion sounds, which indicates if the filament is properly dried, and the like devices.”).
Regarding claim 4, Reese teaches the system of claim 1.
Reese further teaches wherein at least one processor is configured to execute the instructions to perform the step of instructing the robotic arm for removing a defect. ([0096]: “Robotic arm Y can employ a number of interchangeable fixtures to correct defects during part manufacturing. Considering, ‘B’ and ‘C’ defects are detected real-time using a multi-axis system consisting of D devices during build production, where D devices are quality detecting devices. Image processing software identifies the type, location, and number of defects present. This information is fed into the controlling software (feedback controller) for robotic arm Y, which identifies the appropriate course of action to correct the defect;”, where correcting the defect can include removing).
Regarding claim 5, Reese teaches the system of claim 1.
Reese further teaches wherein at least one processor is configured to execute the instructions to perform the step of instructing the robotic arm for repairing a defect. ([0096]: “Robotic arm Y can employ a number of interchangeable fixtures to correct defects during part manufacturing. Considering, ‘B’ and ‘C’ defects are detected real-time using a multi-axis system consisting of D devices during build production, where D devices are quality detecting devices. Image processing software identifies the type, location, and number of defects present. This information is fed into the controlling software (feedback controller) for robotic arm Y, which identifies the appropriate course of action to correct the defect;”, where correcting the defect can include repairing).
Regarding claim 7, Reese teaches the system of claim 1.
Reese further teaches wherein the additive manufacturing process is a robotic arc directed energy deposition additive manufacturing process. ([0029]: “The build apparatus may further comprise a plurality of functional tool heads with plurality of tool attachments that perform different functions associated with and required in the 3D printing process.” “The tool attachments attached to the plurality of functional tool heads perform a variety of functions that may include but are not limited to printing the object by depositing the building material layer by layer while also providing support in printing the object, such as milling bit, deburring tool, cooling means, heating means, and the like.”).
Regarding claim 8, Reese discloses A method for identifying a defect in an additive manufacturing process for a three-dimensional object, the additive manufacturing process ([0008]: “The present invention provides an improved method of additive manufacturing comprising monitoring and identification of defects occurring in a 3D object while it is being printed;”) having predetermined manufacturing conditions,; ([0008]: “A 3D object segment or layer is sliced based on slicing parameters and object property requirements. Tool path instructions for segment or layer are generated and fed to 3D printer for printing.”).
the method comprising: receiving information from one or more sensors during deposition of a metal material ([0084]: “In order to detect such defects, in real time, while the printing process is happening, the system comprises one or more monitoring devices, such as quality detecting devices 400, to continuously monitor the printing process.”).
by a manufacturing robot ([0030]: “For a person skilled in the art, it may be obvious that multi-axis motion mechanism, such as robotic arms or manipulators, are present in the art to provide attachment for a number of tool attachments and allow them to perform various functions.”) during manufacture of a three-dimensional object; ([0002]: “The additive manufacturing process is widely known as the three dimensional printing of 3D objects.”).
receiving information regarding operating characteristics of the one or more sensors ([0084]: “The quality detecting devices 400 may include and are not limited to thermal sensors, cameras, lasers, or any other available image detection devices, infrared devices to check the geometry of the object, audio microphones to capture abnormal extrusion sounds, which indicates if the filament is properly dried, and the like devices.”).
and regarding the metal material’s expected microstructure ([0027]: “In an embodiment, the user device may contain programs such as AutoCad, wherein the user may prepare a CAD file defining the characteristics of the 3D object, including geometrical, mechanical, thermal, chemical, electrical and other property constraints of the object.”) under the predetermined manufacturing conditions; ([0008]: “A 3D object segment or layer is sliced based on slicing parameters and object property requirements. Tool path instructions for segment or layer are generated and fed to 3D printer for printing.”).
weighting the information from one or more sensors based on the received information; and determining, using the weighted information, a first likelihood of a defect in the material. ([0039]: “When the defects are identified by the quality detecting devices, the features of the defects are communicated to the controller, such as a defect feedback controller, where the defects are analyzed and processed.”; [0040] “Depending on the feature of defects, the system 200 stops building the object and generates a build report featuring the defects/errors. This may be the case when the errors are catastrophic (shown by 216).”; [0041] “On the other hand, when the errors are not catastrophic, the defect feedback controller may generate a set of correcting instructions for repairing the defects in the printable layer.”; [0072]: “The build report also compares the actual material properties of the final object compared to the user's inputted specifications and identifies any properties that fall outside the specifications, by implementing analyzing techniques.”, where making a determination of whether the errors are catastrophic is a weight based on operational characteristics of sensors).
Regarding claim 9, Reese teaches the method of claim 8
Reese further teaches further comprising repairing the defect. ([0096]: “Robotic arm Y can employ a number of interchangeable fixtures to correct defects during part manufacturing. Considering, ‘B’ and ‘C’ defects are detected real-time using a multi-axis system consisting of D devices during build production, where D devices are quality detecting devices. Image processing software identifies the type, location, and number of defects present. This information is fed into the controlling software (feedback controller) for robotic arm Y, which identifies the appropriate course of action to correct the defect;”, where correcting the defect can include repairing).
Regarding claim 12, Reese et. al discloses A system for identifying a defect in an additive manufacturing process, (Reese, [0008]: “The present invention provides an improved method of additive manufacturing comprising monitoring and identification of defects occurring in a 3D object while it is being printed;”) the system comprising: a manufacturing robot configured for additive manufacturing of a three-dimensional object ([0094]: “In one manifestation of this present invention, a 3D printing machine involves two independently controlled multi-axis functional tool heads. Say those functional tool heads may be robotic arms, where robotic arm X consists of an extruder and robotic arm Y functions as a real-time correction device.”) from a metal material ([0002]: “Further, the technologies have progressed to where the 3D printing also utilizes higher-end engineering semi-crystalline and amorphous polymers as well as metals and ceramics with greater mechanical, chemical, thermal and electrical properties”) under predetermined manufacturing conditions; ([0008]: “A 3D object segment or layer is sliced based on slicing parameters and object property requirements. Tool path instructions for segment or layer are generated and fed to 3D printer for printing.”) a plurality of sensors positioned to monitor deposited metal material during manufacture of the three-dimensional object, ([0096]: “Robotic arm Y can employ a number of interchangeable fixtures to correct defects during part manufacturing. Considering, ‘B’ and ‘C’ defects are detected real-time using a multi-axis system consisting of D devices during build production, where D devices are quality detecting devices.”, where quality detecting devices include sensors) the sensors each having operational characteristics under the predetermined manufacturing conditions; ([0084]: “The quality detecting devices 400 may include and are not limited to thermal sensors, cameras, lasers, or any other available image detection devices, infrared devices to check the geometry of the object, audio microphones to capture abnormal extrusion sounds, which indicates if the filament is properly dried, and the like devices.”) a predictive index comprising information regarding susceptibility of the deposited metal material to a manufacturing defect; ([0027]: “The characteristics inputted by the user may be object properties that are required in a final object, including and not limited to physical geometry of the object, along with inherent properties like mechanical, electrical, chemical, and/or thermal properties and features that are required to build the object.”; [0062] “After identifying the defects, the quality detecting devices 400 may store the captured data in a data storage module, where the data defines the features of the defect, such as location, type, etc., critical feature measurements, dimensionality, and contour of the build object.”) memory comprising instructions; at least one processor in communication with the manufacturing robot, ([0030]: “Furthermore, the tool attachments also have the ability to orient themselves with multiple degrees of freedom since they are attached to functional tool heads provided with multi-axis motion mechanism. For a person skilled in the art, it may be obvious that multi-axis motion mechanism, such as robotic arms or manipulators, are present in the art to provide attachment for a number of tool attachments and allow them to perform various functions.”; [0031] “The tool attachments of the build apparatus may function based on the tool path instructions 206, and are controlled by a controller.” ) the one or more sensors, ([0084]: “The quality detecting devices 400 may include and are not limited to thermal sensors, cameras, lasers, or any other available image detection devices, infrared devices to check the geometry of the object, audio microphones to capture abnormal extrusion sounds, which indicates if the filament is properly dried, and the like devices.”) the predictive index, and the memory, ([0062]: “After identifying the defects, the quality detecting devices 400 may store the captured data in a data storage module, where the data defines the features of the defect, such as location, type, etc., critical feature measurements, dimensionality, and contour of the build object.”; [0063] “Thereafter, the detected data may be transferred to a controller, such as a defect feedback controller 402 that implements analyzing tools to predict a set of correcting instructions 404.”)
the processor configured to execute the instructions to perform steps comprising: receiving sensor data from each of the plurality of sensors during manufacture of the three-dimensional object; ([0038] “The monitoring devices or the quality detecting devices continuously monitor (210) and identify the printing defects in the object while it is being printed, in real time.”, where monitoring devices or quality detecting devices include sensors)
receiving from the predictive index information regarding susceptibility of the deposited metal material to a manufacturing defect; ([0039]: “When the defects are identified by the quality detecting devices, the features of the defects are communicated to the controller, such as a defect feedback controller, where the defects are analyzed and processed.”; ([0062]: “After identifying the defects, the quality detecting devices 400 may store the captured data in a data storage module, where the data defines the features of the defect, such as location, type, etc., critical feature measurements, dimensionality, and contour of the build object.”; [0063] “Thereafter, the detected data may be transferred to a controller, such as a defect feedback controller 402 that implements analyzing tools to predict a set of correcting instructions 404.”)
assigning a weight to the sensor data from each sensor of the plurality of sensors in part based on the operational characteristics of the plurality of sensors under the manufacturing conditions of the three-dimensional object; and determining a first likelihood of a defect in the deposited material based on the weighted sensor data and the information regarding susceptibility of the deposited metal material to a defect. (Reese, [0039]: “When the defects are identified by the quality detecting devices, the features of the defects are communicated to the controller, such as a defect feedback controller, where the defects are analyzed and processed.”; [0040] “Depending on the feature of defects, the system 200 stops building the object and generates a build report featuring the defects/errors. This may be the case when the errors are catastrophic (shown by 216).”; [0041] “On the other hand, when the errors are not catastrophic, the defect feedback controller may generate a set of correcting instructions for repairing the defects in the printable layer.”; [0072]: “The build report also compares the actual material properties of the final object compared to the user's inputted specifications and identifies any properties that fall outside the specifications, by implementing analyzing techniques.”; [0084]: “The quality detecting devices 400 may include and are not limited to thermal sensors, cameras, lasers, or any other available image detection devices, infrared devices to check the geometry of the object, audio microphones to capture abnormal extrusion sounds, which indicates if the filament is properly dried, and the like devices.”, where making a determination of whether the errors are catastrophic is a weight based on operational characteristics of sensors)., where making a determination of whether the errors are catastrophic is a weight based on operational characteristics of sensors).
Regarding claim 13, Reese teaches the method of claim 12.
Reese further teaches wherein the operational characteristics of the plurality of sensors include one or more of an operating range, error, and reliability under the predetermined manufacturing conditions. [0084]: “The quality detecting devices 400 may include and are not limited to thermal sensors, cameras, lasers, or any other available image detection devices, infrared devices to check the geometry of the object, audio microphones to capture abnormal extrusion sounds, which indicates if the filament is properly dried, and the like devices.”).
Regarding claim 14, Reese teaches the method of claim 12.
Reese further teaches wherein the predictive index comprises a database separate from the memory. (Reese, [0062]: “After identifying the defects, the quality detecting devices 400 may store the captured data in a data storage module, where the data defines the features of the defect, such as location, type, etc., critical feature measurements, dimensionality, and contour of the build object. “).
Regarding claim 15, Reese teaches the method of claim 12.
Reese further teaches wherein the information regarding susceptibility of the deposited material to a defect varies based one or more of the material’s chemical composition, peak temperature, time at temperature, cooling rate, and location within the three-dimensional object. ([0079]: “In another embodiment, characteristics of the object may include and is not limited to structural geometry of the object, inherent material properties, such as mechanical, electrical, chemical, and thermal properties, and the like.”).
Regarding claim 19, Reese teaches the method of claim 12.
Reese further teaches wherein the processor is configured to execute the steps repeatedly as the metal material is deposited during the additive manufacturing process. (Reese, [0096]: “Robotic arm Y can employ a number of interchangeable fixtures to correct defects during part manufacturing. Considering, ‘B’ and ‘C’ defects are detected real-time using a multi-axis system consisting of D devices during build production, where D devices are quality detecting devices.”; [0097] “Further, Robotic arm Y picks up the appropriate fixture with ‘A’ functionality to correct the defect. At this time, robotic arm X can continue to extrude material if the deposition site is not affected; otherwise, robotic arm X waits until robotic arm Y has finished correcting the defect. The defect information is used to update the printed object model and slicing parameters. As a result, the subsequent tool path instructions are modified for robotic arm X. Robotic arm Y repeats the above operation to correct every possible defect during the entire build process.”).
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.
Claims 6 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Reese et. al. (US 20160236414 A1) in view of Buller (US 20230390826 A1).
Regarding claim 6, Reese teaches the system of claim 1.
Reese does not explicitly teach “wherein the metal material is selected from the group consisting of stainless steel, Nickel based alloy, advanced high strength steel, copper alloy, titanium alloy, and aluminum.”
Buller further teaches wherein the metal material is selected from the group consisting of stainless steel, Nickel based alloy, advanced high strength steel, copper alloy, titanium alloy, and aluminum. (Buller, [0190]: “A layer of the 3D object may comprise a single type of material. For example, a layer of the 3D object may comprise a single metal alloy type. In some examples, a layer within the 3D object may comprise several types of material (e.g., an elemental metal and an alloy, several alloy types, several alloy phases, or any combination thereof). In certain embodiments, each type of material comprises only a single member of that type. For example, a single member of metal alloy (e.g., Aluminum Copper alloy). In some cases, a layer of the 3D object comprises more than one type of material. In some cases, a layer of the 3D object comprises more than one member of a material type.”).
Regarding claim 10, Reese teaches the method of claim 9.
Reese does not explicitly teach “further comprising overriding the step of repairing the defect.”
Buller further teaches further comprising overriding the step of repairing the defect. (Buller, [0153]: “Feedforward and/or open loop control may supplement (e.g., override) one or more (e.g., any) corrections and/or predictions (e.g., by the predictor model). The override may be effectuated by forcing a predefined transforming intensity (energy beam power density, or binding agent flux) to supply to the portion (e.g., of the material bed and/or of the 3D object).”).
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 teachings of Reese to incorporate the teachings of Buller so as to include the metals taught by Buller. Doing so would allow for the metals required by the 3D object to be used [0190]: “At times, the pre-transformed material is requested and/or pre-determined for the 3D object. The pre-transformed material can be chosen such that the material is the requested and/or otherwise predetermined material for the 3D object.”).
Claims 11 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Reese et. al. (US 20160236414 A1) in view of Salasoo et. al. (US 20200242096 A1)).
Regarding claim 11, Reese teaches the method of claim 8.
Reese does not explicitly teach “further comprising determining a second likelihood of a defect in the material without using the weighted information based on the information received from the one or more sensors.”
Salasoo further teaches further comprising determining a second likelihood of a defect in the material without using the weighted information based on the information received from the one or more sensors. ([0008]: “The quality of a built part may be estimated using the model and expressed as one or more quality scores.”; [0017]: “The method further includes applying a first algorithm to at least the received sensor data to generate a quality score.”; [0019] “The method may further include determining, using the processor of the device, a second set of build parameters using a second algorithm applied to the received sensor data, the determined quality score, and the thermal data, the second algorithm being trained to improve the quality score;”, where the quality estimate includes a likelihood of a defect).
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 teachings of Reese to incorporate the teachings of Salasoo so as to include determining a second likelihood of a defect. Doing so would reduce the cost and time for part quality assessment ([0007]: “Disclosed embodiments provide for predicting part quality without a physical testing step in every trial build iteration. A part quality model is developed based on sensor measurements made during the part build and other information known at the time of the build. Part quality-based decisions, such as modifications to the PBP, or part accept/reject, are based on the quality model results. Analyzing data generated during the build, and known at the time of analysis, instead of performing post-build testing reduces cost and elapsed time for PBP development, as well as cost and time for production part quality assessment.”).
Regarding claim 16, Reese teaches the method of claim 12.
Reese does not explicitly teach “further comprising determining a second likelihood of a defect in the deposited material based on the sensor data from the plurality of sensors without the weights being applied, wherein the second likelihood differs from the first likelihood.”
Salasoo further teaches further comprising determining a second likelihood of a defect in the deposited material based on the sensor data from the plurality of sensors without the weights being applied, wherein the second likelihood differs from the first likelihood. ([0008]: “The quality of a built part may be estimated using the model and expressed as one or more quality scores.”; [0017]: “The method further includes applying a first algorithm to at least the received sensor data to generate a quality score.”; [0019] “The method may further include determining, using the processor of the device, a second set of build parameters using a second algorithm applied to the received sensor data, the determined quality score, and the thermal data, the second algorithm being trained to improve the quality score;”, where the quality estimate includes a likelihood of a defect).
Claims 17 is rejected under 35 U.S.C. 103 as being unpatentable over Reese et. al. (US 20160236414 A1), in view of Dimatteo et. al. (US 20160368220 A1).
Regarding claim 17, Reese teaches the method of claim 12.
Reese does not explicitly teach “wherein each sensor of the plurality of sensors has adjustable acceptance criteria that are compared with respective sensor data.”
Dimatteo further teaches wherein each sensor of the plurality of sensors has adjustable acceptance criteria that are compared with respective sensor data. (Dimatteo, [0015]: “In one example, these differences can be compared to a pass or fail criteria for part screening. For example, the differences can be compared to a predetermined tolerance level and the scanned 3D object passes the evaluation if the differences are within the predetermined tolerance level to allow the method 100 to evaluate any level of details.”).
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 teachings of Reese to incorporate the teachings of Dimatteo so as to include sensors having adjustable acceptance criteria that are compared to the respective sensor data. Doing so would allow for better detection of manufacturing errors occurring during the additive manufacturing process (Dimatteo, [0003]: “If these errors could be detected, the manufacture could be adjusted to compensate for the errors. Some previously known systems do not evaluate the production of the object. In these systems, the production of the 3D object with defects can result in a poor quality 3D object or inaccurate reproduction of the 3D object. As such, improvements in the production process that enable evaluation of a 3D object are desirable.”).
Claims 18 is rejected under 35 U.S.C. 103 as being unpatentable over Reese et. al. (US 20160236414 A1), in view of Riemann et. al. (US 11858042 B2).
Regarding claim 18, Reese teaches the method of claim 12.
Reese does not explicitly teach “wherein the step of assigning a weight to the sensor data comprises ignoring one or more sensors of the plurality of sensors.”
From the same field of endeavor, Riemann teaches wherein the step of assigning a weight to the sensor data comprises ignoring one or more sensors of the plurality of sensors. (Riemann, [48]: “Data capture component 203 may be configured to control, for example, when data is captured from various aspects of additive manufacturing system 100, as well as the resolution of that data (e.g., the frequency of data sampling in samples/second or Hz). For example, data capture component 203 may be configured to disable data capture when additive manufacturing system 100 is not depositing material, but enable data capture when additive manufacturing system is depositing material. Similarly, data capture component 203 may be configured to turn on and off sensors based on which aspects of additive manufacturing system 100 are active.”).
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 teachings of Reese to incorporate the teachings of Riemann so as to include ignoring one or more sensors when assigning a weight to the sensor data. Doing so would allow for optimal process parameters to be obtained during the additive manufacturing process ([5]: “Accordingly, improved systems and methods are needed for determining optimal process parameters for additive manufacturing processes.”).
Claims 20 is rejected under 35 U.S.C. 103 as being unpatentable over Reese et. al. (US 20160236414 A1) in view of Roychowdhury et. al. (US 20200242495 A1).
Regarding claim 20, Reese teaches the method of claim 12.
Reese does not explicitly teach “wherein the step of determining a first likelihood of a defect in the deposited material comprises performing a weighted average.”
Roychowdhury further teaches wherein the step of determining a first likelihood of a defect in the deposited material comprises performing a weighted average. ([0011]: “Disclosed embodiments provide for predicting part quality without a physical testing step in every trial build iteration. A part quality model is developed based on sensor measurements made during the part build and other information known at the time of the build.”; [0013] “In disclosed embodiments, quality scores may be determined for different anomalies, such as, for example, pore density, crack density, and lack-of-fusion defect density. A single overall score may be derived from a combination of multiple sub-scores, e.g., sum, weighted sum, maximum, average, weighted average.”, where predicting part quality involves likelihood of a defect).
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 teachings of Reese to incorporate the teachings of Roychowdhury so as to include the step of determining a first likelihood of defect in the deposited material comprising a weighted average. Doing so would save allow for reduction in time and cost of developing new additively manufactured parts [0005]: “In particular, DMLM parts may be sectioned, optical micrographs produced from the processed section, and the micrographs processed to quantify anomalies. The assessment of trial part quality is based on such tests. Such testing is laborious, expensive, and time-consuming, and significantly increases the time and cost of developing an acceptable PBP to release to final production.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 12111638 B2: Adaptive Production System
US 20230302539 A1: Tool for Scan Path Visualization and defect Distribution Prediction
US 20230062971 A1: Apparatus, Systems, And Methods For Monitoring, Analyzing, And Adjusting Additive Machine And Build Health And Configuration
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/N.P.Z./Examiner, Art Unit 2117
/ROBERT E FENNEMA/Supervisory Patent Examiner, Art Unit 2117
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