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

  • Authors:

  • Massimo Pacella;Quirico Semeraro

  • Affiliations:

  • Dipartimento di Ingegneria dell'Innovazione, Universití degli Studi di Lecce,Via per Monteroni, Lecce 73100, Italy;Dipartimento di Meccanica, Politecnico di Milano, Via Bonardi, Milano 20133, Italy

  • Venue:
  • Engineering Applications of Artificial Intelligence
  • Year:
  • 2005

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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.