US 12,447,687 B2
Systems, methods, and media for artificial intelligence process control in additive manufacturing
Vadim Pinskiy, Wayne, NJ (US); Matthew C. Putman, Brooklyn, NY (US); Damas Limoge, Brooklyn, NY (US); and Aswin Raghav Nirmaleswaran, Brooklyn, NY (US)
Assigned to Nanotronics Imaging, Inc., Cuyahoga Falls, OH (US)
Filed by Nanotronics Imaging, Inc., Cuyahoga Falls, OH (US)
Filed on Aug. 21, 2023, as Appl. No. 18/452,914.
Application 18/452,914 is a continuation of application No. 17/444,619, filed on Aug. 6, 2021, granted, now 11,731,368.
Application 17/444,619 is a continuation of application No. 16/853,640, filed on Apr. 20, 2020, granted, now 11,084,225, issued on Aug. 10, 2021.
Application 16/853,640 is a continuation in part of application No. 16/723,212, filed on Dec. 20, 2019, granted, now 11,097,490, issued on Aug. 24, 2021.
Application 16/723,212 is a continuation of application No. PCT/US2019/024795, filed on Mar. 29, 2019.
Application 16/723,212 is a continuation of application No. 15/943,442, filed on Apr. 2, 2018, granted, now 10,518,480, issued on Dec. 31, 2019.
Claims priority of provisional application 62/836,199, filed on Apr. 19, 2019.
Claims priority of provisional application 62/836,202, filed on Apr. 19, 2019.
Claims priority of provisional application 62/836,213, filed on Apr. 19, 2019.
Claims priority of provisional application 62/898,535, filed on Sep. 10, 2019.
Prior Publication US 2023/0391016 A1, Dec. 7, 2023
Int. Cl. B29C 64/393 (2017.01); B22F 10/30 (2021.01); B22F 10/85 (2021.01); B22F 12/90 (2021.01); B29C 64/209 (2017.01); B33Y 10/00 (2015.01); B33Y 50/02 (2015.01); G06F 18/20 (2023.01); G06F 18/2411 (2023.01); G06N 3/04 (2023.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 10/98 (2022.01); B22F 10/12 (2021.01); B22F 10/18 (2021.01); B22F 10/25 (2021.01); B22F 10/28 (2021.01)
CPC B29C 64/393 (2017.08) [B22F 10/30 (2021.01); B22F 10/85 (2021.01); B22F 12/90 (2021.01); B29C 64/209 (2017.08); B33Y 10/00 (2014.12); B33Y 50/02 (2014.12); G06F 18/2411 (2023.01); G06F 18/295 (2023.01); G06N 3/04 (2013.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 10/993 (2022.01); B22F 10/12 (2021.01); B22F 10/18 (2021.01); B22F 10/25 (2021.01); B22F 10/28 (2021.01)] 20 Claims
OG exemplary drawing
 
1. A method of training a reinforcement learning model for performing a corrective action in a manufacturing process executing in a manufacturing system, the method comprising:
receiving, by a computing system, an image of a specimen at a processing node in the manufacturing process;
detecting, by the computing system, an error in the specimen based on the image of the specimen;
determining, by the computing system using a reinforcement learning model, a change to a manufacturing parameter to correct the error based on a policy;
determining, by the computing system, state information of the specimen, the state information comprising a current action performed on the specimen and a previous action performed by the specimen at an upstream processing node in the manufacturing process;
generating, by the computing system, a quality metric for the specimen based on the state information;
generating, by the computing system, a reward corresponding to the state information;
comparing, by the computing system, an expected reward corresponding to the state information to the generated reward;
determining, by the computing system, that there is a deviation between the reward and the expected reward that exceeds a threshold amount; and
based on the determining, updating, by the computing system, the policy implemented by the reinforcement learning model.