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dc.creatorBabić, Bojan
dc.creatorNesić, Nenad
dc.creatorMiljković, Zoran
dc.date.accessioned2022-09-19T16:39:06Z
dc.date.available2022-09-19T16:39:06Z
dc.date.issued2011
dc.identifier.issn0890-0604
dc.identifier.urihttps://machinery.mas.bg.ac.rs/handle/123456789/1228
dc.description.abstractFeature technology is considered an essential tool for integrating design and manufacturing. Automatic feature recognition (AFR) has provided the greatest contribution to fully automated computer-aided process planning system development. The objective of this paper is to review approaches based on application of artificial neural networks for solving major AFR problems. The analysis presented in this paper shows which approaches are suitable for different individual applications and how far away we are from the formation of a general AFR algorithm.en
dc.publisherCambridge Univ Press, New York
dc.rightsrestrictedAccess
dc.sourceAi Edam-Artificial Intelligence For Engineering Design Analysis and Manufacturing
dc.subjectNeural Networksen
dc.subjectISO 10303 Standard Seriesen
dc.subjectFeature Extractionen
dc.subjectComputer-Aided Process Planningen
dc.titleAutomatic feature recognition using artificial neural networks to integrate design and manufacturing: Review of automatic feature recognition systemsen
dc.typearticle
dc.rights.licenseARR
dc.citation.epage304
dc.citation.issue3
dc.citation.other25(3): 289-304
dc.citation.rankM22
dc.citation.spage289
dc.citation.volume25
dc.identifier.doi10.1017/S0890060410000545
dc.identifier.scopus2-s2.0-80054963117
dc.identifier.wos000293378000006
dc.type.versionpublishedVersion


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