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Article: Automatic recognition of machining features from computer aided design part models

TitleAutomatic recognition of machining features from computer aided design part models
Authors
Issue Date2000
PublisherProfessional Engineering Publishing Ltd. The Journal's web site is located at http://journals.pepublishing.com/link.asp?id=119784
Citation
Proceedings Of The Institution Of Mechanical Engineers, Part B: Journal Of Engineering Manufacture, 2000, v. 214 n. 6, p. 515-520 How to Cite?
AbstractThis paper presents an innovative method for recognizing manufacturing features from computer aided design (CAD) part models. The proposed method is to integrate the graph-based approach and the volume approach, in order to combine the positive aspects of both strategies. Feature edge sequence, a new graph-based feature recognition approach, is used to recognize and extract surface features from the part design data. The extracted features are then clustered into the machining volumes by the volume-based approach. The main drawback of conventional feature recognition systems is their limitations in handling feature interactions and arbitrary shape features. In the proposed system, the graph-based method is able to recognize interacting features, the volume-based approach can generate alternative interpretations of machining volumes and an artificial intelligence (AI)-based algorithm, established with a neural network, is used to handle the arbitrary features.
Persistent Identifierhttp://hdl.handle.net/10722/177626
ISSN
2015 Impact Factor: 0.978
2015 SCImago Journal Rankings: 0.541
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorWong, TNen_US
dc.contributor.authorLam, SMen_US
dc.date.accessioned2012-12-19T09:38:26Z-
dc.date.available2012-12-19T09:38:26Z-
dc.date.issued2000en_US
dc.identifier.citationProceedings Of The Institution Of Mechanical Engineers, Part B: Journal Of Engineering Manufacture, 2000, v. 214 n. 6, p. 515-520en_US
dc.identifier.issn0954-4054en_US
dc.identifier.urihttp://hdl.handle.net/10722/177626-
dc.description.abstractThis paper presents an innovative method for recognizing manufacturing features from computer aided design (CAD) part models. The proposed method is to integrate the graph-based approach and the volume approach, in order to combine the positive aspects of both strategies. Feature edge sequence, a new graph-based feature recognition approach, is used to recognize and extract surface features from the part design data. The extracted features are then clustered into the machining volumes by the volume-based approach. The main drawback of conventional feature recognition systems is their limitations in handling feature interactions and arbitrary shape features. In the proposed system, the graph-based method is able to recognize interacting features, the volume-based approach can generate alternative interpretations of machining volumes and an artificial intelligence (AI)-based algorithm, established with a neural network, is used to handle the arbitrary features.en_US
dc.languageengen_US
dc.publisherProfessional Engineering Publishing Ltd. The Journal's web site is located at http://journals.pepublishing.com/link.asp?id=119784en_US
dc.relation.ispartofProceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufactureen_US
dc.titleAutomatic recognition of machining features from computer aided design part modelsen_US
dc.typeArticleen_US
dc.identifier.emailWong, TN: tnwong@hku.hken_US
dc.identifier.authorityWong, TN=rp00192en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1243/0954405001517810-
dc.identifier.scopuseid_2-s2.0-0033717361en_US
dc.identifier.hkuros55087-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0033717361&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume214en_US
dc.identifier.issue6en_US
dc.identifier.spage515en_US
dc.identifier.epage520en_US
dc.identifier.isiWOS:000088748400009-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridWong, TN=55301015400en_US
dc.identifier.scopusauthoridLam, SM=7402279658en_US

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