US 12,125,190 B1
Conformance testing of manufactured parts via neural networks
William Paul King, Urbana, IL (US); Sameh Tawfick, Urbana, IL (US); Miles Bimrose, Urbana, IL (US); Charles Wood, Itasca, IL (US); and Davis McGregor, Itasca, IL (US)
Assigned to Sybridge Technologies U.S. Inc., Southfield, MI (US); and The Board of Trustees of the University of Illinois, Urbana, IL (US)
Filed by SyBridge Technologies U.S. Inc., Southfield, MI (US); and The Board of Trustees of the University of Illinois, Urbana, IL (US)
Filed on Apr. 8, 2024, as Appl. No. 18/629,281.
Int. Cl. G06T 7/00 (2017.01)
CPC G06T 7/0004 (2013.01) [G06T 2207/10116 (2013.01); G06T 2207/20084 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computing system comprising:
one or more processors; and
memory storing program instructions that, upon execution by the one or more processors, cause the computing system to perform operations comprising:
obtaining an image of at least a section of a manufactured part;
determining, based on executing a neural network on the image, that the manufactured part was not fabricated according to a specification for the manufactured part, wherein the neural network was trained to associate images of manufactured parts with corresponding indicators of specifications for the manufactured parts, wherein determining that the manufactured part was not fabricated according to the specification comprises:
generating, by the neural network, a first fabrication signature vector embedding of the manufactured part, and
determining that the first fabrication signature vector embedding is within a predetermined threshold distance of a second fabrication signature vector embedding of a known defective manufactured part; and
in response to determining that the manufactured part was not fabricated according to the specification, generating an electronic alert indicating that the manufactured part was improperly fabricated, wherein the electronic alert also indicates that the manufactured part was fabricated by a same manufacturing machine as the known defective manufactured part.