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:
Jan. 21, 2025

Filed:

Aug. 12, 2021
Applicant:

The Boeing Company, Chicago, IL (US);

Inventors:

Troy Winfree, Seattle, WA (US);

Sayata Ghose, Sammamish, WA (US);

Brice A. Johnson, Federal Way, WA (US);

Dustin Fast, Owens Cross Roads, AL (US);

Assignee:

The Boeing Company, Arlington, VA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); B29C 70/34 (2006.01); B29C 70/54 (2006.01); B29C 73/24 (2006.01); B32B 3/14 (2006.01); B32B 3/18 (2006.01); B32B 37/06 (2006.01); B32B 37/10 (2006.01); B32B 37/18 (2006.01); B32B 38/18 (2006.01); B32B 41/00 (2006.01); G06T 7/12 (2017.01); G06T 7/33 (2017.01);
U.S. Cl.
CPC ...
G06T 7/001 (2013.01); B29C 70/34 (2013.01); B29C 70/54 (2013.01); B29C 73/24 (2013.01); B32B 3/14 (2013.01); B32B 3/18 (2013.01); B32B 37/06 (2013.01); B32B 37/10 (2013.01); B32B 37/18 (2013.01); B32B 38/1808 (2013.01); B32B 41/00 (2013.01); G06T 7/12 (2017.01); G06T 7/344 (2017.01); B32B 2041/04 (2013.01); B32B 2309/72 (2013.01); B32B 2605/18 (2013.01); G06T 2207/10048 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20132 (2013.01); G06T 2207/30108 (2013.01);
Abstract

A method of detecting defects in a composite layup includes capturing, using an infrared camera, reference images of a reference layup being laid up by a reference layup head. The method also includes manually reviewing the reference images for defects, and generating reference defect masks indicating defects in the reference images. The method further includes training, using the reference images and reference defect masks, a neural network, creating a machine learning model that, given a production image as input, outputs a production defect mask indicating the defect location and the defect type of each defect. The method also includes capturing, using an infrared camera, production images of a production layup being laid up by the production layup head, and applying the model to the production images to automatically generate a production defect masks indicating each defect in the production images.


Find Patent Forward Citations

Loading…