US 12,253,837 B2
Causal relational artificial intelligence and risk framework for manufacturing applications
Jeremy Bellay, Columbus, OH (US); Shelly DeForte, Canal Winchester, OH (US); Nicholas Darby, Columbus, OH (US); and Kurtis Wickey, Springfield, OH (US)
Assigned to Battelle Memorial Institute, Columbus, OH (US)
Filed by Battelle Memorial Institute, Columbus, OH (US)
Filed on Apr. 22, 2022, as Appl. No. 17/726,617.
Claims priority of provisional application 63/178,982, filed on Apr. 23, 2021.
Prior Publication US 2022/0342371 A1, Oct. 27, 2022
Int. Cl. G05B 13/02 (2006.01); G06F 18/214 (2023.01); G06F 18/2323 (2023.01)
CPC G05B 13/027 (2013.01) [G06F 18/214 (2023.01); G06F 18/2323 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for manufacturing process control, the computer-implemented method comprising:
identifying, by one or more computer processors, an intervention;
collecting, by the one or more computer processors, process dependency data;
creating, by the one or more computer processors, an intervention model;
combining, by the one or more computer processors, the process dependency data and the intervention model to create a combined process dependency graph;
training, by the one or more computer processors, a causal relational artificial intelligence (CRAI) model with the combined process dependency graph using a Graph Neural Network;
determining, by the one or more computer processors, whether a causal relationship exists between the intervention and an outcome based on the CRAI model; and
responsive to determining that the causal relationship exists between the intervention and the outcome, determining, by the one or more computer processors, an Intervention Efficacy Estimate for manufacturing process control based on the combined process dependency graph and the CRAI model.