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Article: A neural network system for two-dimensional feature recognition

TitleA neural network system for two-dimensional feature recognition
Authors
Issue Date1998
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, 1998, v. 11 n. 2, p. 111-117 How to Cite?
AbstractRecognition of geometric features is important for automatic evaluation of part designs and development of process plans. This paper describes an implementation of a neural network for feature recognition in sheet metal parts created in a CAD system. One major part of the implementation is the development of a rotation- and translation-insensitive encoding scheme which extracts critical information from geometric features and candidate geometric loops. The successful implementation has led to a powerful system where end users can customize the domain of features that can be recognized. Training of the neural network memory is also achieved through a user-friendly graphic interface. ©1998 Taylor & Francis Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/156296
ISSN
2015 Impact Factor: 1.319
2015 SCImago Journal Rankings: 0.673
References

 

DC FieldValueLanguage
dc.contributor.authorChen, YHen_US
dc.contributor.authorLee, HMen_US
dc.date.accessioned2012-08-08T08:41:52Z-
dc.date.available2012-08-08T08:41:52Z-
dc.date.issued1998en_US
dc.identifier.citationInternational Journal Of Computer Integrated Manufacturing, 1998, v. 11 n. 2, p. 111-117en_US
dc.identifier.issn0951-192Xen_US
dc.identifier.urihttp://hdl.handle.net/10722/156296-
dc.description.abstractRecognition of geometric features is important for automatic evaluation of part designs and development of process plans. This paper describes an implementation of a neural network for feature recognition in sheet metal parts created in a CAD system. One major part of the implementation is the development of a rotation- and translation-insensitive encoding scheme which extracts critical information from geometric features and candidate geometric loops. The successful implementation has led to a powerful system where end users can customize the domain of features that can be recognized. Training of the neural network memory is also achieved through a user-friendly graphic interface. ©1998 Taylor & Francis Ltd.en_US
dc.languageengen_US
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/0951192X.aspen_US
dc.relation.ispartofInternational Journal of Computer Integrated Manufacturingen_US
dc.titleA neural network system for two-dimensional feature recognitionen_US
dc.typeArticleen_US
dc.identifier.emailChen, YH:yhchen@hkucc.hku.hken_US
dc.identifier.authorityChen, YH=rp00099en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-0000863719en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0000863719&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume11en_US
dc.identifier.issue2en_US
dc.identifier.spage111en_US
dc.identifier.epage117en_US
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridChen, YH=7601430448en_US
dc.identifier.scopusauthoridLee, HM=8454962200en_US

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