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Author(s): Pacella, M. | Semeraro, Q. |
Year: 2005
Citation: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE Volume: 18 Issue: 6 Pages: 645-662
Abstract: 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.
Topics:
Machine Learning,
Applications:
Industrial Control,
Models:
ART 2 / Fuzzy ART,
