Understanding ART-based neural algorithms as statistical tools for manufacturing process quality control
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 ne...
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| Published in | Engineering applications of artificial intelligence Vol. 18; no. 6; pp. 645 - 662 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.09.2005
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0952-1976 1873-6769 |
| DOI | 10.1016/j.engappai.2005.02.001 |
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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. |
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| AbstractList | 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. |
| Author | Pacella, Massimo Semeraro, Quirico |
| Author_xml | – sequence: 1 givenname: Massimo surname: Pacella fullname: Pacella, Massimo email: massimo.pacella@unile.it organization: Dipartimento di Ingegneria dell’Innovazione, Università degli Studi di Lecce,Via per Monteroni, Lecce 73100, Italy – sequence: 2 givenname: Quirico surname: Semeraro fullname: Semeraro, Quirico organization: Dipartimento di Meccanica, Politecnico di Milano, Via Bonardi, Milano 20133, Italy |
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| Cites_doi | 10.1080/00224065.1984.11978921 10.1016/S0893-6080(96)00018-4 10.1016/S0360-8352(96)00310-5 10.1080/07408179308964288 10.1111/j.2517-6161.1994.tb01990.x 10.1016/0360-8352(94)00024-H 10.1016/j.engappai.2003.11.005 10.1080/00207540410001715706 10.1016/S0893-6080(02)00063-1 10.1080/00207549508904783 10.1080/07408179808966453 10.1080/002075499191148 10.1016/S0360-8352(99)00004-2 10.1016/0893-6080(94)00073-U 10.1080/002075497195650 10.1023/A:1008818817588 10.2307/1270528 10.1080/01621459.1997.10474027 10.1080/00207540110061616 10.1080/00207540110071750 10.1016/0360-8352(93)90010-U 10.1080/00031305.1994.10476030 10.1016/S0893-6080(99)00031-3 10.1080/002075499190987 10.1080/00207549608905024 10.1080/00401706.1996.10484497 |
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article-title: Neural networks in applied statistics publication-title: Technometrics doi: 10.1080/00401706.1996.10484497 |
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| SubjectTerms | Adaptive resonance theory Artificial intelligence Cluster analysis Neural network design Statistical process control |
| Title | Understanding ART-based neural algorithms as statistical tools for manufacturing process quality control |
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