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Automatic feature recognition using artificial neural networks to integrate design and manufacturing: Review of automatic feature recognition systems
dc.creator | Babić, Bojan | |
dc.creator | Nesić, Nenad | |
dc.creator | Miljković, Zoran | |
dc.date.accessioned | 2022-09-19T16:39:06Z | |
dc.date.available | 2022-09-19T16:39:06Z | |
dc.date.issued | 2011 | |
dc.identifier.issn | 0890-0604 | |
dc.identifier.uri | https://machinery.mas.bg.ac.rs/handle/123456789/1228 | |
dc.description.abstract | Feature 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.publisher | Cambridge Univ Press, New York | |
dc.rights | restrictedAccess | |
dc.source | Ai Edam-Artificial Intelligence For Engineering Design Analysis and Manufacturing | |
dc.subject | Neural Networks | en |
dc.subject | ISO 10303 Standard Series | en |
dc.subject | Feature Extraction | en |
dc.subject | Computer-Aided Process Planning | en |
dc.title | Automatic feature recognition using artificial neural networks to integrate design and manufacturing: Review of automatic feature recognition systems | en |
dc.type | article | |
dc.rights.license | ARR | |
dc.citation.epage | 304 | |
dc.citation.issue | 3 | |
dc.citation.other | 25(3): 289-304 | |
dc.citation.rank | M22 | |
dc.citation.spage | 289 | |
dc.citation.volume | 25 | |
dc.identifier.doi | 10.1017/S0890060410000545 | |
dc.identifier.scopus | 2-s2.0-80054963117 | |
dc.identifier.wos | 000293378000006 | |
dc.type.version | publishedVersion |