Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, BT | - |
dc.contributor.author | Park, MW | - |
dc.contributor.author | Kim, SK | - |
dc.date.accessioned | 2024-01-21T11:33:26Z | - |
dc.date.available | 2024-01-21T11:33:26Z | - |
dc.date.created | 2021-09-05 | - |
dc.date.issued | 2001-12 | - |
dc.identifier.issn | 0268-3768 | - |
dc.identifier.uri | https://pubs.kist.re.kr/handle/201004/139989 | - |
dc.description.abstract | In metal cutting processes, cutting conditions have an influence on reducing the production cost and time and deciding the quality of a final product. This paper presents a new methodology for continual improvement of cutting conditions. It is called GELCC (generation and evolutionary learning of cutting conditions). GELCC is a key component of an operation planning system for milling operations. It performs the following three functions. 1. The modification of recommended cutting conditions obtained from a machining data handbook. 2. The incremental learning of obtained cutting conditions using fuzzy ARTMAP neural networks. 3. The substitution of better cutting conditions for those learned previous by a proposed replacement algorithm. Various simulations illustrate the performance of GELCC. and then the simulation results for a given part are provided and discussed. | - |
dc.language | English | - |
dc.publisher | SPRINGER-VERLAG LONDON LTD | - |
dc.subject | OPTIMIZATION | - |
dc.subject | SYSTEM | - |
dc.subject | ART | - |
dc.title | Generation and evolutionary learning of cutting conditions for milling operations | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/s001700170098 | - |
dc.description.journalClass | 1 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, v.17, no.12, pp.870 - 880 | - |
dc.citation.title | INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY | - |
dc.citation.volume | 17 | - |
dc.citation.number | 12 | - |
dc.citation.startPage | 870 | - |
dc.citation.endPage | 880 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.identifier.wosid | 000169770700002 | - |
dc.identifier.scopusid | 2-s2.0-0034802949 | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Manufacturing | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalResearchArea | Engineering | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | OPTIMIZATION | - |
dc.subject.keywordPlus | SYSTEM | - |
dc.subject.keywordPlus | ART | - |
dc.subject.keywordAuthor | computer-aided process planning (CAPP) | - |
dc.subject.keywordAuthor | cutting conditions | - |
dc.subject.keywordAuthor | neural network | - |
dc.subject.keywordAuthor | operation planning | - |
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