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Article: A hybrid expert-neural-based function model for CAPP

TitleA hybrid expert-neural-based function model for CAPP
Authors
Issue Date1997
PublisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/0951192X.asp
Citation
International Journal Of Computer Integrated Manufacturing, 1997, v. 10 n. 1-4, p. 105-116 How to Cite?
AbstractOne of the important activities in computer integrated manufacturing (CIM) is computer aided process planning (CAPP). Although many CAPP systems have been reported in the literature, very few of them have actually been used in practice. This is largely due to the complex and dynamic nature of the process planning task, and the difficulties associated with representing and manipulating the required knowledge. In this paper, a hybrid expert-neural-based CAPP intelligent function model (CAPPIFM) and its detailed architecture are presented. Such a model is designed on the basis of the experience gained in developing an integrated CAPP system called SIP which is supported by the National 863 High Technology Plan of China. Indeed, the model provides an effective means for achieving an intelligent, dynamic, parallel and distributed integration of the CAPP system with the other systems in the CIM environment. © 1997 Taylor & Francis Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/74356
ISSN
2023 Impact Factor: 3.7
2023 SCImago Journal Rankings: 0.987
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorMing, XGen_HK
dc.contributor.authorMak, KLen_HK
dc.contributor.authorYan, JQen_HK
dc.contributor.authorMa, DZen_HK
dc.contributor.authorZhang, HQen_HK
dc.date.accessioned2010-09-06T07:00:32Z-
dc.date.available2010-09-06T07:00:32Z-
dc.date.issued1997en_HK
dc.identifier.citationInternational Journal Of Computer Integrated Manufacturing, 1997, v. 10 n. 1-4, p. 105-116en_HK
dc.identifier.issn0951-192Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/74356-
dc.description.abstractOne of the important activities in computer integrated manufacturing (CIM) is computer aided process planning (CAPP). Although many CAPP systems have been reported in the literature, very few of them have actually been used in practice. This is largely due to the complex and dynamic nature of the process planning task, and the difficulties associated with representing and manipulating the required knowledge. In this paper, a hybrid expert-neural-based CAPP intelligent function model (CAPPIFM) and its detailed architecture are presented. Such a model is designed on the basis of the experience gained in developing an integrated CAPP system called SIP which is supported by the National 863 High Technology Plan of China. Indeed, the model provides an effective means for achieving an intelligent, dynamic, parallel and distributed integration of the CAPP system with the other systems in the CIM environment. © 1997 Taylor & Francis Ltd.en_HK
dc.languageengen_HK
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/0951192X.aspen_HK
dc.relation.ispartofInternational Journal of Computer Integrated Manufacturingen_HK
dc.titleA hybrid expert-neural-based function model for CAPPen_HK
dc.typeArticleen_HK
dc.identifier.emailMak, KL:makkl@hkucc.hku.hken_HK
dc.identifier.authorityMak, KL=rp00154en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/095119297131228-
dc.identifier.scopuseid_2-s2.0-0000215003en_HK
dc.identifier.hkuros28393en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0000215003&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume10en_HK
dc.identifier.issue1-4en_HK
dc.identifier.spage105en_HK
dc.identifier.epage116en_HK
dc.identifier.isiWOS:A1997WG52600010-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridMing, XG=7005300183en_HK
dc.identifier.scopusauthoridMak, KL=7102680226en_HK
dc.identifier.scopusauthoridYan, JQ=7403728727en_HK
dc.identifier.scopusauthoridMa, DZ=7402075358en_HK
dc.identifier.scopusauthoridZhang, HQ=8139437200en_HK
dc.identifier.issnl0951-192X-

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