Understanding ART-based neural algorithms as statistical tools for manufacturing process quality control

• Published in: Engineering applications of artificial intelligence Vol. 18; no. 6; pp. 645 - 662
• Main Authors: Pacella, Massimo, Semeraro, Quirico
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2005
• Subjects: Adaptive resonance theory; Artificial intelligence; Cluster analysis; Neural network design; Statistical process control; Cluster analysis; Statistical process control; Adaptive resonance theory; Neural network design; Artificial intelligence
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2005.02.001

Neural networks have recently received a great deal of attention in the field of manufacturing process quality control, where statistical techniques have traditionally been used. In this paper, a neural-based procedure for quality monitoring is discussed from a statistical perspective. The neural network is based on Fuzzy ART, which is exploited for recognising any unnatural change in the state of a manufacturing process. Initially, the neural algorithm is analysed by means of geometrical arguments. Then, in order to evaluate control performances in terms of errors of Types I and II, the effects of three tuneable parameters are examined through a statistical model. Upper bound limits for the error rates are analytically computed, and then numerically illustrated for different combinations of the tuneable parameters. Finally, a criterion for the neural network designing is proposed and validated in a specific test case through simulation. The results demonstrate the effectiveness of the proposed neural-based procedure for manufacturing quality monitoring.