DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Kyung Sam | ko |
dc.contributor.author | Kim, Soung Hie | ko |
dc.date.accessioned | 2008-05-21T09:31:06Z | - |
dc.date.available | 2008-05-21T09:31:06Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 1998 | - |
dc.identifier.citation | ARTIFICIAL INTELLIGENCE IN ENGINEERING, v.12, no.1-2, pp.127 - 134 | - |
dc.identifier.issn | 0954-1810 | - |
dc.identifier.uri | http://hdl.handle.net/10203/4651 | - |
dc.description.abstract | In Computer Numerical Control (CNC) machining, determining optimum or appropriate cutting parameters can minimize machining errors such as tool breakage, tool deflection and tool wear, thus yielding a high productivity or minimum cost. There have been a number of attempts to determine the machining parameters through off-line adjustment or on-line adaptive control. These attempts use many different kinds of techniques: CAD-based approaches, Operations Research approaches, and Artificial Intelligence (AI) approaches. After describing an overview of these approaches, we will focus on reviewing Al-based techniques for providing a better understanding of these techniques in machining control. AI-based methods fall into three categories: knowledge-based expert systems approach, neural networks approach and probabilistic inference approach. In particular, recent research interests mainly tend to develop on-line or real-time expert systems for adapting machining parameters. The use of AI techniques would be valuable for the purpose. (C) 1997 Elsevier Science Limited. | - |
dc.language | English | - |
dc.language.iso | en_US | en |
dc.publisher | ELSEVIER SCI LTD | - |
dc.subject | INFLUENCE DIAGRAMS | - |
dc.subject | OPTIMIZATION | - |
dc.title | Artificial intelligence approaches to determination of CNC machining parameters in manufacturing: a review | - |
dc.type | Article | - |
dc.identifier.wosid | 000071068300010 | - |
dc.identifier.scopusid | 2-s2.0-0031696430 | - |
dc.type.rims | ART | - |
dc.citation.volume | 12 | - |
dc.citation.issue | 1-2 | - |
dc.citation.beginningpage | 127 | - |
dc.citation.endingpage | 134 | - |
dc.citation.publicationname | ARTIFICIAL INTELLIGENCE IN ENGINEERING | - |
dc.identifier.doi | 10.1016/S0954-1810(97)00011-3 | - |
dc.embargo.liftdate | 9999-12-31 | - |
dc.embargo.terms | 9999-12-31 | - |
dc.contributor.localauthor | Kim, Soung Hie | - |
dc.contributor.nonIdAuthor | Park, Kyung Sam | - |
dc.type.journalArticle | Review | - |
dc.subject.keywordAuthor | CNC machining | - |
dc.subject.keywordAuthor | machining parameter | - |
dc.subject.keywordAuthor | knowledge-based expert system | - |
dc.subject.keywordAuthor | neural network | - |
dc.subject.keywordAuthor | influence diagram | - |
dc.subject.keywordPlus | INFLUENCE DIAGRAMS | - |
dc.subject.keywordPlus | OPTIMIZATION | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.