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Proceedings Paper

Control chart pattern recognition using a back propagation neural network
Author(s): Julie K. Spoerre; Marcus B. Perry
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Paper Abstract

In this paper, control chart pattern recognition using artificial neural networks is presented. An important motivation of this research is the growing interest in intelligent manufacturing systems, specifically in the area of Statistical Process Control (SPC). On-line automated process analysis is an important area of research since it allows the interfacing of process control with Computer Integrated Manufacturing (CIM) techniques. A back propagation artificial neural network is used to model X-bar quality control charts and identify process instability situations as specified by the Western Electric Statistical Quality Control handbook. Results indicate that the performance of the back propagation neural network is very accurate in identifying these control chart patterns. This work is significant in that the neural network output can serve as a link to process parameters in a closed-loop control system. In this way, adjustments to the process can be made on-line and quality problems averted.

Paper Details

Date Published: 13 October 2000
PDF: 11 pages
Proc. SPIE 4192, Intelligent Systems in Design and Manufacturing III, (13 October 2000); doi: 10.1117/12.403658
Show Author Affiliations
Julie K. Spoerre, Southern Illinois Univ. (United States)
Marcus B. Perry, Southeast Missouri State Univ. (United States)

Published in SPIE Proceedings Vol. 4192:
Intelligent Systems in Design and Manufacturing III
Bhaskaran Gopalakrishnan; Angappa Gunasekaran, Editor(s)

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