Manufacturing process quality control by means of a Fuzz'y ART neural network algorithm

Neural networks are potential tools that can be used to improve process quality control. In fact, various neural algorithms have been applied successfully for detecting groups of well-defined unnatural patterns in the output measurements of manufacturing processes. This paper discusses the use of a neural network as a means for recognising changes in the state of the monitored process, rather than for identifying a restricted set of unnatural patterns on the output data. In particular, a control algorithm, which is based on the Fuzzy ART neural network, is first presented, and then studied in a specific reference case by means of Monte Carlo simulation. Comparisons between the performances of the proposed neural approach, and those of the CUSUM control chart, are also presented in the paper. The results indicate that the proposed neural network is a practical alternative to the existing control schemes.