The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 24, 2014

Filed:

Oct. 01, 2012
Applicant:

GM Global Technology Operations Llc, Detroit, MI (US);

Inventors:

Jeffrey A Abell, Rochester Hills, MI (US);

John Patrick Spicer, Plymouth, MI (US);

Michael Anthony Wincek, Rochester, MI (US);

Hui Wang, Highland, MI (US);

Debejyo Chakraborty, Sterling Heights, MI (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
B23K 1/06 (2006.01); B23K 5/20 (2006.01); B23K 20/00 (2006.01); B23K 20/10 (2006.01); G05B 11/00 (2006.01); G05B 11/06 (2006.01); B23K 20/12 (2006.01); G05B 1/00 (2006.01); G05B 1/01 (2006.01); G05B 1/04 (2006.01); G05B 11/01 (2006.01); G05B 19/00 (2006.01); G05B 19/02 (2006.01); G05B 19/04 (2006.01); G05B 19/18 (2006.01);
U.S. Cl.
CPC ...
B23K 20/10 (2013.01); B23K 20/106 (2013.01); B23K 20/12 (2013.01); G05B 1/00 (2013.01); G05B 1/01 (2013.01); G05B 1/04 (2013.01); G05B 11/00 (2013.01); G05B 11/01 (2013.01); G05B 11/011 (2013.01); G05B 19/00 (2013.01); G05B 19/02 (2013.01); G05B 19/04 (2013.01); G05B 19/18 (2013.01);
Abstract

A system includes host and learning machines in electrical communication with sensors positioned with respect to an item of interest, e.g., a weld, and memory. The host executes instructions from memory to predict a binary quality status of the item. The learning machine receives signals from the sensor(s), identifies candidate features, and extracts features from the candidates that are more predictive of the binary quality status relative to other candidate features. The learning machine maps the extracted features to a dimensional space that includes most of the items from a passing binary class and excludes all or most of the items from a failing binary class. The host also compares the received signals for a subsequent item of interest to the dimensional space to thereby predict, in real time, the binary quality status of the subsequent item of interest.


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