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Proceedings Paper

Machine-vision-based quality control decision making for naturally varying product
Author(s): Wayne D. Daley; Sergio Grullon; Douglas F. Britton
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Paper Abstract

The application of machine vision system to industrial manufacturing and inspection processes has motivate the development of intelligent and yet flexible decision making processes. When working with highly uniform product, most of the quality or inspection decisions can be based on straightforward but rigid rules once the relevant features have been extracted from the image. However when the product is highly nonuniform, other techniques must be applied to allow for product variability while still being capable of identifying and classifying defects. This paper will investigate methods for accomplishing this based on soft computing. A discussion of the general approach and then a specific methods for accomplishing this based on soft computing. A discussion of the general approach and then a specific examples of an integrated system for product quality determination is presented. This system combines color image processing and feature extraction with neural network classifiers and fuzzy logic based decision outputs to allow for maximum flexibility in accommodating product variability while still maintaining quality standards. The techniques for optimizing the classification parameters and the determination of the fuzzy logic membership functions and user rules are presented.

Paper Details

Date Published: 27 August 1999
PDF: 10 pages
Proc. SPIE 3836, Machine Vision Systems for Inspection and Metrology VIII, (27 August 1999); doi: 10.1117/12.360276
Show Author Affiliations
Wayne D. Daley, Georgia Tech Research Institute (United States)
Sergio Grullon, Georgia Tech Research Institute (United States)
Douglas F. Britton, Georgia Tech Research Institute (United States)


Published in SPIE Proceedings Vol. 3836:
Machine Vision Systems for Inspection and Metrology VIII
John W. V. Miller; Susan Snell Solomon; Bruce G. Batchelor, Editor(s)

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