US 12,386,320 B2
Controlling a manufacturing process using causal models
Brian E. Brooks, St. Paul, MN (US); Gilles J. Benoit, Minneapolis, MN (US); Peter O. Olson, Andover, MN (US); Tyler W. Olson, Woodbury, MN (US); Himanshu Nayar, St. Paul, MN (US); Frederick J. Arsenault, Stillwater, MN (US); and Nicholas A. Johnson, Burnsville, MN (US)
Assigned to 3M Innovative Properties Company, St. Paul, MN (US)
Appl. No. 17/437,563
Filed by 3M INNOVATIVE PROPERTIES COMPANY, St. Paul, MN (US)
PCT Filed Sep. 11, 2019, PCT No. PCT/IB2019/057662
§ 371(c)(1), (2) Date Sep. 9, 2021,
PCT Pub. No. WO2020/188329, PCT Pub. Date Sep. 24, 2020.
Claims priority of provisional application 62/818,816, filed on Mar. 15, 2019.
Prior Publication US 2022/0128979 A1, Apr. 28, 2022
Int. Cl. G05B 13/04 (2006.01); B60W 40/064 (2012.01); B60W 40/08 (2012.01); B60W 40/105 (2012.01); G05B 13/02 (2006.01); G05B 19/4065 (2006.01); G05B 19/418 (2006.01); G05B 23/02 (2006.01); G06F 18/21 (2023.01); G06N 5/043 (2023.01); G06N 5/046 (2023.01); G06N 7/01 (2023.01); G06Q 10/0631 (2023.01); G06Q 10/0639 (2023.01); G06Q 30/0202 (2023.01); G06Q 10/087 (2023.01)
CPC G05B 13/042 (2013.01) [B60W 40/064 (2013.01); B60W 40/08 (2013.01); B60W 40/105 (2013.01); G05B 13/021 (2013.01); G05B 13/024 (2013.01); G05B 13/0265 (2013.01); G05B 13/041 (2013.01); G05B 19/4065 (2013.01); G05B 19/41835 (2013.01); G05B 23/0229 (2013.01); G05B 23/0248 (2013.01); G06F 18/2193 (2023.01); G06N 5/043 (2013.01); G06N 5/046 (2013.01); G06N 7/01 (2023.01); G06Q 10/06315 (2013.01); G06Q 10/06395 (2013.01); G06Q 30/0202 (2013.01); G05B 2219/36301 (2013.01); G06Q 10/087 (2013.01)] 13 Claims
OG exemplary drawing
 
1. A method for optimizing control settings of a manufacturing process, the method comprising:
repeatedly performing the following:
selecting a configuration of control settings for a manufacturing process, based on a set of internal control parameters and a causal model mapped to a probability distribution over possible control settings and a set of internal control parameters that define how to update the causal model, such that the causal model measures causal relationships between control settings and a measure of a success of the manufacturing process, wherein control settings are assigned to different procedural instances that allow blocked groups to later be identified in order to compute impact measurements between blocked groups, wherein probability matching is utilized to map the impact measurements and confidence intervals for the manufacturing process in the causal model to probabilities and the control settings are assigned based on these probabilities;
determining the measure of the success of the manufacturing process using the configuration of control settings;
adjusting, based on the measure of the success of the manufacturing process using the configuration of control settings, the causal model; and
re-computing the causal model by computing overall impact measurements based on confidence intervals around the overall impact measurements and means of d-scores.