Assembly line inspection using neural networks

Alastair D. McAulay; Paul Danset; Devert W. Wicker

doi:10.1117/12.21328

1 September 1990 Assembly line inspection using neural networks

Alastair D. McAulay, Paul Danset, Devert W. Wicker

Proceedings Volume 1297, Hybrid Image and Signal Processing II; (1990) https://doi.org/10.1117/12.21328
Event: 1990 Technical Symposium on Optics, Electro-Optics, and Sensors, 1990, Orlando, FL, United States

Abstract

A user friendly flexible system for assembly line part inspection which learns good and bad parts is described. The system detects missing rivets and springs in clutch drivers. The system extracts features in a circular region of interest from a video image processes these using a Fast Fourier Transform for rotation invariance and uses this as inputs to a neural network trained with back-propagation. The advantage of a learning system is that expensive reprogramming and delays are avoided when a part is modified. Two cases were considered. The first one could use back lighting in that surface effects could be ignored. The second case required front lighting because the part had a cover which prevented light from passing through the parts. 100 percent classification of good and bad parts was achieved for both back-lit and front-lit cases with a limited number of training parts available. 1. BACKGROUND A vision system to inspect clutch drivers for missing rivets and springs at the Harrison Radiator Plant of General Motors (GM) works only on parts without covers Fig. 1 and is expensive. The system does not work when there are cover plates Fig. 2 that rule out back light passing through the area of missing rivets and springs. Also the system like all such systems must be reprogrammed at significant time and cost when the system needs to classify a different fault or a

Citation Download Citation

Alastair D. McAulay, Paul Danset, and Devert W. Wicker "Assembly line inspection using neural networks", Proc. SPIE 1297, Hybrid Image and Signal Processing II, (1 September 1990); https://doi.org/10.1117/12.21328

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