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A Neuro-fuzzy Approach to Machine Vision Based Parts Inspection

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3 Author(s)
J. Killing ; Department of Mechanical and Materials Engineering, Queen's University, Kingston, Ontario, Canada K7L 3N6 ; B. W. Surgenor ; C. K. Mechefske

This paper documents progress on a project whose objective is to improve the performance of a machine vision based parts inspection system through the development and testing of robust neuro-fuzzy based algorithms. An inspection problem faced by a Canadian automotive parts manufacturer is being used as a case study. The problem involves a vision system that is being used to confirm the placement of metal fastening clips on a structural member that supports a truck dash panel. It took the manufacturer over 8 months to tune their commercial machine vision system to detect missing clips. It is hypothesized that a neuro-fuzzy based approach could provide for faster tuning of their vision system. Preliminary results show strong performance of the neuro-fuzzy system and a new algorithm is being developed on this basis to automatically learn the inspection process from a series of training images

Published in:

NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society

Date of Conference:

3-6 June 2006