US 12,443,173 B2
Systems and methods for composite fabrication with AI quality control modules
Matthew J. deFreese, Holdrege, NE (US); Judah Crowe, Kearney, NE (US); and John Loucks, Sauble Beach (CA)
Assigned to Royal Engineered Composites, Inc., Minden (NE)
Filed by Royal Engineered Composites, Inc., Minden, NE (US)
Filed on Oct. 11, 2022, as Appl. No. 17/963,864.
Claims priority of provisional application 63/254,641, filed on Oct. 12, 2021.
Prior Publication US 2023/0112264 A1, Apr. 13, 2023
Int. Cl. G05B 19/418 (2006.01)
CPC G05B 19/41875 (2013.01) [G05B 19/4183 (2013.01); G05B 19/41885 (2013.01); G05B 2219/32368 (2013.01)] 37 Claims
OG exemplary drawing
 
1. A quality control system comprising:
a controller configured to be communicatively coupled with a monitoring assembly including one or more detectors, wherein the controller includes one or more processors configured to execute program instructions causing the one or more processors to implement two or more artificial intelligence quality control (AIQC) modules associated with two or more process steps of a plurality of process steps for fabricating a composite material from two or more plies, wherein each of the two or more AIQC modules is associated with a different one of the two or more process steps, wherein a particular one of the two or more AIQC modules associated with a particular process step of the two or more process steps is configured to:
receive monitoring data from the monitoring assembly associated with the particular process step for a workpiece, the workpiece including at least one of a mold or any of the two or more plies, wherein the monitoring data includes at least one of data associated with the workpiece or an operator associated with fabricating the composite material at the particular process step;
generate quality control data for the particular process step using a particular artificial intelligence (AI) model based on input data including at least the monitoring data associated with the particular process step, the quality control data including at least a pass indicator or a fail indicator for the particular process step, wherein the particular AI model is trained on a training dataset including at least additional monitoring data associated with the particular process step associated with additional workpieces labeled with the pass indicator and the fail indicator; and
update the particular AI model based on testing data associated with the workpiece from one or more testing tools after at least the particular process step.