Control chart pattern recognition using a back propagation neural network

Julie K. Spoerre; Marcus B. Perry

doi:10.1117/12.403658

13 October 2000 Control chart pattern recognition using a back propagation neural network

Julie K. Spoerre, Marcus B. Perry

Proceedings Volume 4192, Intelligent Systems in Design and Manufacturing III; (2000) https://doi.org/10.1117/12.403658
Event: Intelligent Systems and Smart Manufacturing, 2000, Boston, MA, United States

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.

Citation Download Citation

Julie K. Spoerre and Marcus B. Perry "Control chart pattern recognition using a back propagation neural network", Proc. SPIE 4192, Intelligent Systems in Design and Manufacturing III, (13 October 2000); https://doi.org/10.1117/12.403658

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