US 12,449,792 B2
Predictive process control for a manufacturing process
Matthew C. Putman, Brooklyn, NY (US); John B. Putman, Celebration, FL (US); Vadim Pinskiy, Wayne, NJ (US); and Damas Limoge, Brooklyn, NY (US)
Assigned to Nanotronics Imaging, Inc., Cuyahoga Falls, OH (US)
Filed by Nanotronics Imaging, Inc., Cuyahoga Falls, OH (US)
Filed on Apr. 8, 2024, as Appl. No. 18/629,541.
Application 18/629,541 is a continuation of application No. 18/329,265, filed on Jun. 5, 2023, granted, now 12,153,411.
Application 18/329,265 is a continuation of application No. 17/304,613, filed on Jun. 23, 2021, granted, now 11,669,078, issued on Jun. 6, 2023.
Application 17/304,613 is a continuation of application No. 16/519,102, filed on Jul. 23, 2019, granted, now 11,156,991, issued on Oct. 26, 2021.
Claims priority of provisional application 62/865,859, filed on Jun. 24, 2019.
Prior Publication US 2024/0329625 A1, Oct. 3, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G05B 13/02 (2006.01); G05B 19/418 (2006.01)
CPC G05B 19/41875 (2013.01) [G05B 13/027 (2013.01); G05B 2219/32193 (2013.01); G05B 2219/32194 (2013.01); G05B 2219/32195 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving a first set of in-process inputs from a first processing station, at a deep learning controller, wherein the first set of in-process inputs are generated at the first processing station deployed in a manufacturing process, wherein the first set of in-process inputs are attributes of the first processing station of a plurality of stations in the manufacturing process;
identifying, by the deep learning controller, a final quality metric of a plurality of final quality metrics for which to optimize the manufacturing process;
generating, by the deep learning controller, an expected value of the final quality metric for an article of manufacture based on the first set of in-process inputs;
determining, by the deep learning controller, based on a comparison of the first set of in-process inputs and the expected value that anomalous activity is present; and
based on the determining, adjusting, by the deep learning controller, downstream parameters of a downstream processing station to cause the final quality metric to address the anomalous activity.