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Robust Machine Vision-Based Parts Inspection: Intelligent Neuro-Fuzzy Versus Threshold Based Classification

[+] Author Affiliations
Jonathan Killing, Brian W. Surgenor, Chris K. Mechefske

Queen’s University, Kingston, ON, Canada

Paper No. MSEC2007-31196, pp. 537-545; 9 pages
doi:10.1115/MSEC2007-31196
From:
  • ASME 2007 International Manufacturing Science and Engineering Conference
  • ASME 2007 International Manufacturing Science and Engineering Conference
  • Atlanta, Georgia, USA, October 15–18, 2007
  • Conference Sponsors: Manufacturing Division
  • ISBN: 0-7918-4290-8 | eISBN: 0-7918-3809-9
  • Copyright © 2007 by ASME

abstract

Past experience with an industrial machine vision-based parts inspection system highlighted the need for a robust system, that is a vision system that could adapt to changes in the operating environment without requiring excessive retuning of the data analysis algorithm. With this need in mind, an intelligent neuro-fuzzy based image processing algorithm was developed and tested against a traditional threshold based algorithm. Experimental results indicate that the intelligent algorithm performs well when the data is not well segmented.

Copyright © 2007 by ASME
Topics: Machinery , Inspection

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