Author(s)

D.T. Pham & A.B. Chan

Abstract

Statistical Process Control Charts can exhibit six principal types of patterns: Normal, Cyclic, Increasing Trend, Decreasing Trend, Upward Shift and Downward Shift. All except Normal patterns indicate abnormalities in the process that must be corrected. Accurate and speedy detection of such patterns is important to achieving tight control of the process and ensuring good product quality. This paper describes an implementation of the Kohonen self-organising map which employs the Euclidean Distance as the firing rule for control chart pattern recognition. First, the structure of the network is outlined and the equations which govern its dynamics are given. Then the learning mechanism of the ne

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