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Understanding ART-based neural algorithms as statistical tools for manufacturing process quality control
- Source :
-
Engineering Applications of Artificial Intelligence . Sep2005, Vol. 18 Issue 6, p645-662. 18p. - Publication Year :
- 2005
-
Abstract
- 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. [Copyright &y& Elsevier]
- Subjects :
- *ARTIFICIAL neural networks
*ALGORITHMS
*QUALITY control
*MANUFACTURING processes
Subjects
Details
- Language :
- English
- ISSN :
- 09521976
- Volume :
- 18
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Engineering Applications of Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 17948254
- Full Text :
- https://doi.org/10.1016/j.engappai.2005.02.001