DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/28/26 has been entered.
Response to Arguments
Applicant's arguments filed 01/28/26 have been fully considered but they are not persuasive. Applicant argues that Dave does not teach that a corresponding acceptable process variation is associated with a state of progression of the build when the sensor signal(s) are generated, e.g., a distinct location during the build. Instead, Applicant argues, paragraph [0006] of Dave only indicates that the measured state variables are associated with a distinct location in the build.
Applicant further notes that Dave discloses in paragraph [0008] that the state variables can be compared to a known-good range of a baseline dataset, and that this baseline dataset defines the plurality of acceptable ranges. Applicant then argues that there is no disclosure in Dave of each of the plurality of ranges of the baseline dataset is associated with a state of progression of the build. More specifically, Applicant further argues that Dave does not disclose that a baseline dataset is specific to a particular location in the build, but rather that Dave using the some baseline dataset (process windows) for all locations in the build.
However, in response to Applicant’s assertion Dave’s baseline dataset is not specific to a particular location in the build, Examiner notes that Dave teaches “determining a baseline dataset for producing a part includes at least the following steps: collecting temperature data captured by multiple sensors for each layer deposited during each of a number of additive manufacturing operations for constructing the part” (e.g., [0010]). Therefore, the baseline data set of Dave, which corresponds to the claimed “plurality of acceptable process variations” or “fingerprint of sensing data”, is specific to each layer in the build progression or order.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 6-9, 11-12, 17-20, and 22-25 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Dave et al. US 2016/0184893 (“Dave”).
Dave discloses:
1. A method of monitoring an additive manufacturing apparatus comprising an optical scanner comprising tiltable mirrors (e.g., Fig. 4A #409, [0047]: “laser beam 407 enters scan head 409. Scan head 409 can include internal x-deflection, y-deflection, and focusing optics”, [0061]: “these x and y positions may be controlled by mirrors which are actuated by high frequency response galvanometers”) for scanning a laser beam across a powder bed to consolidate powder (e.g., Fig. 4A #401, [0047]: “volume of powder”), and a photodetector (e.g., Fig. 9 #412, [0048]: “photodiode”) mounted to receive radiation that travels from the powder bed to the photodetector via reflection by the tiltable mirrors (e.g., [0048]: “Melt pool 410 emits optical radiation 411 that travels back through scan head 409 and passes through partially reflective mirror 408 to be collected by optical sensor 412”), the photodetector generating sensor signals in response to the received radiation (e.g., [0048]), the method comprising:
receiving the sensor signals generated by the photodetector during a build (e.g., [0006]: “The in-process measurements can be provided by sensors configured to precisely monitor a temperature of the weld pool as it constructs the part”);
comparing each sensor signal or a value derived from a plurality of the sensor signals to a corresponding acceptable process variation of a plurality of acceptable process variations for the build (e.g., [0008]: “comparing the plurality of state variables with a plurality of ranges associated with state variables of the known-good range of the baseline dataset”); and
generating a log based upon the comparisons (e.g., [0006]: “temperature variations exceeding a particular threshold can be recorded for later analysis that can lead to a determination of whether or not a part meets a set of quality assurance standards”, [0033]: “analysis can be applied to the recorded data to determine a quality of each layer of the part…recorded temperatures for each part can be compared and contrasted with temperature data recorded during the production of parts having acceptable material properties”),
wherein each acceptable process variation of the plurality of acceptable process variations is associated with a state of progression of the build and the corresponding acceptable process variation is the acceptable process variation associated with the state of progression during the build when the sensor signal or the plurality of the sensor signals are generated (e.g., [0006]: “State variables can be derived from the aforementioned sensor measurements (i.e., measurements that characterize the current state or evolution over time of the in-process physical behaviors) and be used to determine the presence of any micro-structural variations or even cracks occurring as a result of variations detected by the sensors. Exemplary state variables include cooling rates, heating rates, peak temperature and phase change information that can be associated with various locations for distinct locations on each layer of a part created by the additive manufacturing operation”, [0010]: “determining a baseline dataset for producing a part includes at least the following steps: collecting temperature data captured by multiple sensors for each layer deposited during each of a number of additive manufacturing operations for constructing the part”).
2. A method according to claim 1, wherein the state of progression is an order in which the sensor signals are generated in the build (e.g., [0006]: “State variables can be derived from the aforementioned sensor measurements (i.e., measurements that characterize the current state or evolution over time of the in-process physical behaviors) and be used to determine the presence of any micro-structural variations or even cracks occurring as a result of variations detected by the sensors. Exemplary state variables include cooling rates, heating rates, peak temperature and phase change information that can be associated with various locations for distinct locations on each layer of a part created by the additive manufacturing operation”).
3. A method according to claim 2, wherein the acceptable process variations are in an ordered list corresponding to the order in which the sensor signals are generated during the build and the method comprises determining the corresponding acceptable process variation from the order in which the sensor signal or the plurality of the sensor signals are generated during the build (e.g., [0006]: “State variables can be derived from the aforementioned sensor measurements (i.e., measurements that characterize the current state or evolution over time of the in-process physical behaviors) and be used to determine the presence of any micro-structural variations or even cracks occurring as a result of variations detected by the sensors. Exemplary state variables include cooling rates, heating rates, peak temperature and phase change information that can be associated with various locations for distinct locations on each layer of a part created by the additive manufacturing operation”).
6. A method according to claim 1, wherein the photodetector is a photodiode (e.g., Fig. 9 #412, [0048]: “photodiode”).
7. A method according to claim 1, comprising comparing each value derived from the plurality of the sensor signals to the corresponding acceptable process variation (e.g., [0008]: “comparing the plurality of state variables with a plurality of ranges associated with state variables of the known-good range of the baseline dataset”).
8. A method according to claim 7, wherein the plurality of the sensor signals is collected over tens of microseconds (e.g., [0081]).
9. A method according to claim 7, wherein the value is a mean of the plurality of the sensor signals (e.g., [0090]).
11. A method according to claim 1 comprising using a mapping to transform the acceptable process variations determined for one position in a build volume to acceptable process variations suitable for a position in the build volume for the build in which the sensor signals are generated (e.g., [0008]).
12. A method of monitoring an additive manufacturing apparatus comprising an optical scanner comprising tiltable mirrors (e.g., Fig. 4A #409, [0047]: “laser beam 407 enters scan head 409. Scan head 409 can include internal x-deflection, y-deflection, and focusing optics”, [0061]: “these x and y positions may be controlled by mirrors which are actuated by high frequency response galvanometers”) for scanning a laser beam across a powder bed to consolidate powder (e.g., Fig. 4A #401, [0047]: “volume of powder”), and a photodetector (e.g., Fig. 9 #412, [0048]: “photodiode”) mounted to receive radiation that travels from the powder bed to the photodetector via reflection by the tiltable mirrors (e.g., [0048]: “Melt pool 410 emits optical radiation 411 that travels back through scan head 409 and passes through partially reflective mirror 408 to be collected by optical sensor 412”), the photodetector generating sensor signals in response to the received radiation (e.g., [0048]), the method comprising:
receiving the sensor signals generated by the photodetector during a build (e.g., [0006]: “The in-process measurements can be provided by sensors configured to precisely monitor a temperature of the weld pool as it constructs the part”);
comparing the sensor signals or values derived from a plurality of the sensor signals to a fingerprint of sensing data for states of progression of the build, the sensing data derived from builds evaluated as meeting specified requirements (e.g., [0008]: “comparing the plurality of state variables with a plurality of ranges associated with state variables of the known-good range of the baseline dataset”); and
generating a log based upon the comparisons (e.g., [0006]: “temperature variations exceeding a particular threshold can be recorded for later analysis that can lead to a determination of whether or not a part meets a set of quality assurance standards”, [0033]: “analysis can be applied to the recorded data to determine a quality of each layer of the part…recorded temperatures for each part can be compared and contrasted with temperature data recorded during the production of parts having acceptable material properties”);
wherein the fingerprint comprises an ordered list of sensing data corresponding to the order in which the sensor signals are generated during the build and the method comprises comparing the sensor signals to corresponding sensing data of the fingerprint from the order in which the sensor signal or the plurality of the sensor signals are generated during the build (e.g., [0006]: “State variables can be derived from the aforementioned sensor measurements (i.e., measurements that characterize the current state or evolution over time of the in-process physical behaviors) and be used to determine the presence of any micro-structural variations or even cracks occurring as a result of variations detected by the sensors. Exemplary state variables include cooling rates, heating rates, peak temperature and phase change information that can be associated with various locations for distinct locations on each layer of a part created by the additive manufacturing operation”, [0010]: “determining a baseline dataset for producing a part includes at least the following steps: collecting temperature data captured by multiple sensors for each layer deposited during each of a number of additive manufacturing operations for constructing the part”).
17. A method according to claim 12, wherein the photodetector is a photodiode (e.g., Fig. 9 #412, [0048]: “photodiode”).
18. A method according to claim 12, comprising comparing each value derived from the plurality of the sensor signals to the fingerprint of sensing data (e.g., [0006]-[0008], [0053]).
19. A method according to claim 18, wherein the plurality of the sensor signals is collected over tens of microseconds (e.g., [0081]).
20. A method according to claim 18, wherein the value is a mean of the plurality of the sensor signals (e.g., [0090]).
22. A method according to claim 12 comprising using a mapping to transform the fingerprint determined for one position in a build volume to a fingerprint suitable for a position in the build volume for the build in which the sensor signals are generated (e.g., [0008]).
23. A method of monitoring an additive manufacturing apparatus comprising a scanner (e.g., Fig. 4A #409) for scanning an energy beam (e.g., Fig. 4A #407) across a powder bed to consolidate powder of the powder bed (e.g., Fig. 4A #401) at a plurality of exposure points, the method comprising:
receiving sensor signals from the additive manufacturing apparatus during a build of a workpiece (e.g., [0006]: “The in-process measurements can be provided by sensors configured to precisely monitor a temperature of the weld pool as it constructs the part”), the sensor signals generated when exposure points on the powder bed are exposed to the energy beam to consolidate powder (e.g., Fig. 5 #501, [0053]: “optical sensor 412 will operate at a finite sampling rate as the beam scans the area of the witness coupon, and it will collect data at discrete sampling locations 501”),
comparing each sensor signal or a value derived from a plurality of the sensor signals to a corresponding acceptable process variation of a plurality of acceptable process variations for the build (e.g., [0008]: “comparing the plurality of state variables with a plurality of ranges associated with state variables of the known-good range of the baseline dataset”); and
generating a log based upon the comparisons (e.g., [0006]: “temperature variations exceeding a particular threshold can be recorded for later analysis that can lead to a determination of whether or not a part meets a set of quality assurance standards”, [0033]: “analysis can be applied to the recorded data to determine a quality of each layer of the part…recorded temperatures for each part can be compared and contrasted with temperature data recorded during the production of parts having acceptable material properties”),
wherein each acceptable process variation of the plurality of acceptable process variations is associated with an exposure point (e.g., Fig. 5 #501, [0053]: “optical sensor 412 will operate at a finite sampling rate as the beam scans the area of the witness coupon, and it will collect data at discrete sampling locations 501”) of the build and the corresponding acceptable process variation is the acceptable process variation associated with the exposure point being exposed to the energy beam when the sensor signal or the plurality of sensor signals are generated (e.g., [0006]: “State variables can be derived from the aforementioned sensor measurements (i.e., measurements that characterize the current state or evolution over time of the in-process physical behaviors) and be used to determine the presence of any micro-structural variations or even cracks occurring as a result of variations detected by the sensors. Exemplary state variables include cooling rates, heating rates, peak temperature and phase change information that can be associated with various locations for distinct locations on each layer of a part created by the additive manufacturing operation”, [0010]: “determining a baseline dataset for producing a part includes at least the following steps: collecting temperature data captured by multiple sensors for each layer deposited during each of a number of additive manufacturing operations for constructing the part”).
24. A method according to claim 23, wherein the scanner comprises tiltable mirrors (e.g., Fig. 4A #409, [0047]: “laser beam 407 enters scan head 409. Scan head 409 can include internal x-deflection, y-deflection, and focusing optics”, [0061]: “these x and y positions may be controlled by mirrors which are actuated by high frequency response galvanometers”) for scanning a laser beam across the powder bed to consolidate powder and a photodetector (e.g., Fig. 9 #412, [0048]: “photodiode”) mounted to receive radiation from the powder bed to the photodetector via reflection by the tiltable mirrors (e.g., [0048]: “Melt pool 410 emits optical radiation 411 that travels back through scan head 409 and passes through partially reflective mirror 408 to be collected by optical sensor 412”), wherein the photodetector generates the sensor signals in response to the received radiation (e.g., [0048]).
25. A method according to claim 23, wherein there is a one-to-one correlation between exposure points and acceptable process variations (e.g., [0006]-[0008], [0053]).
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 10 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Dave in view of Blackmore EP 2832475 A2.
Dave does not explicitly disclose the features of claims 10 and 21.
Blackmore (in combination with Dave) discloses:
10. A method according to claim 1, comprising displaying a 2- or 3- dimensional representation identifying suspect regions of the build, which, during formation, generated sensor signals outside the corresponding acceptable process variations (e.g., [0060], [0066]-[0067]).
21. A method according to claim 12, comprising displaying a 2- or 3- dimensional representation identifying suspect regions of the build identified from comparing the sensor signals or values derived from a plurality of the sensor signals to a fingerprint of sensing data (e.g., [0060], [0066]-[0067]).
Dave and Blackmore are analogous arts since both pertain to quality determination in additive manufacturing systems.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Dave with Blackmore in order to provide the user of Dave with a visual representation of the defects so that knowledge of the defects can be grasped more quickly, thus facilitating the correction process and preventing product waste.
Claims 4, 5, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Dave in view of Herzog US 10,265,912.
Dave does not appear to explicitly disclose the features of claims 4, 5, 15, and 16.
Herzog (in combination with Dave) discloses:
4. A method according to claim 1, wherein the state of progression is a time from a set event in the build (e.g., col. 4 lines 1-13, claim 10).
5. A method according to claim 2, wherein each acceptable process variation is associated with at least one build time and the method comprises determining the corresponding acceptable process variation from a build time when the sensor signal or the plurality of the sensor signals are generated during the build (e.g., col. 4 lines 1-13, claim 10).
15. A method according to claim 12, wherein the states of progression are times from a set event in the build (e.g., col. 4 lines 1-13, claim 10).
16. A method according to claim 12, wherein the sensing data is associated with build times and the method comprises comparing the sensor signals to corresponding sensing data of the fingerprint determined from a build time when the sensor signal or the plurality of the sensor signals are generated during the build (e.g., col. 4 lines 1-13, claim 10).
Dave and Herzog are analogous arts since both pertain to quality determination in additive manufacturing systems.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Dave with Herzog since Herzog teaches that such time-offset capturing methods help to avoid overheating effects in the case of very fine component interior regions (col. 4 lines 1-13).
Conclusion
All claims are identical to or patentably indistinct from, or have unity of invention with claims in the application prior to the entry of the submission under 37 CFR 1.114 (that is, restriction (including a lack of unity of invention) would not be proper) and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR 1.114. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN A JARRETT whose telephone number is (571)272-3742. The examiner can normally be reached M-F 9:00-5:30.
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/RYAN A JARRETT/Primary Examiner, Art Unit 2116
02/21/26