US 11,055,639 B1
Optimizing manufacturing processes using one or more machine learning models
Pelin Cay, Raleigh, NC (US); Nabaruna Karmakar, Morrisville, NC (US); Natalia Summerville, Cary, NC (US); Varunraj Valsaraj, Cary, NC (US); Antony Nicholas Cooper, Knoxville, TN (US); Steven Joseph Gardner, Cary, NC (US); and Joshua David Griffin, Harrisburg, NC (US)
Assigned to SAS INSTITUTE INC., Cary, NC (US)
Filed by SAS Institute Inc., Cary, NC (US)
Filed on Oct. 6, 2020, as Appl. No. 17/64,280.
Claims priority of provisional application 63/016,767, filed on Apr. 28, 2020.
Int. Cl. G06N 20/00 (2019.01); G06N 3/08 (2006.01); G06Q 10/04 (2012.01); G06F 9/54 (2006.01); G06N 3/02 (2006.01); G06N 20/20 (2019.01); G06N 20/10 (2019.01); G06N 3/04 (2006.01); G06F 9/50 (2006.01)
CPC G06Q 10/04 (2013.01) [G06F 9/547 (2013.01); G06N 3/02 (2013.01); G06N 20/00 (2019.01); G06F 9/5011 (2013.01); G06F 9/54 (2013.01); G06N 3/0454 (2013.01); G06N 3/0472 (2013.01); G06N 3/0481 (2013.01); G06N 20/10 (2019.01); G06N 20/20 (2019.01)] 30 Claims
OG exemplary drawing
1. A system comprising:
one or more processing devices; and
one or more memory devices including instructions that are executable by the one or more processing devices for causing the one or more processing devices to:
execute an optimization model to identify a recommended set of values for configurable settings of a manufacturing process associated with an object, the optimization model being configured to determine the recommended set of values by implementing an iterative process using an objective function, each iteration in a plurality of iterations of the iterative process including:
selecting a current set of candidate values for the configurable settings from within a current region of a search space defined by the optimization model, the current set of candidate values being selected for use in a current iteration of the iterative process;
providing the current set of candidate values as input to a trained machine learning model that is separate from the optimization model, wherein the trained machine learning model is configured to be trained prior to an initiation of the iterative process, the trained machine learning model being configured to predict a value for a target characteristic of the object or the manufacturing process based on the current set of candidate values;
receiving the value as output from the trained machine learning model; and
identifying a next region of the search space to use in a next iteration of the iterative process based on the value.