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Proceedings Paper

Assembly line inspection using neural networks
Author(s): Alastair D. McAulay; Paul Danset; Devert W. Wicker
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Paper 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

Paper Details

Date Published: 1 September 1990
PDF: 11 pages
Proc. SPIE 1297, Hybrid Image and Signal Processing II, (1 September 1990); doi: 10.1117/12.21328
Show Author Affiliations
Alastair D. McAulay, Wright State Univ. (United States)
Paul Danset, Wright State Univ. (United States)
Devert W. Wicker, Wright State Univ. (United States)

Published in SPIE Proceedings Vol. 1297:
Hybrid Image and Signal Processing II
David P. Casasent; Andrew G. Tescher, Editor(s)

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