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스테인레스 박강판의 레이저 점 용접 시 음향방출 실시간 모니터링

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dc.contributor.author이성환-
dc.contributor.author최정욱-
dc.contributor.author최장은-
dc.date.accessioned2021-06-24T00:06:07Z-
dc.date.available2021-06-24T00:06:07Z-
dc.date.issued2005-04-
dc.identifier.issn1225-9071-
dc.identifier.issn2287-8769-
dc.identifier.urihttps://scholarworks.bwise.kr/erica/handle/2021.sw.erica/46381-
dc.description.abstractCompared with conventional welding , laser spot welding offers a unique combination of high speed, precision and low heat distortion. This combination of advantages is attractive for manufacturing industries including automotive and electronics companies. In this paper, a real time monitoring scheme for a pulsed Nd:YAG laser spot welding was suggested. Acoustic emission (AE) signals were collected during welding and analyzed for given process conditions such as laser power and pulse duration. A back propagation artificial neural network, with AE frequency content inputs, was used to predict the weldability of stainless steel sheets.-
dc.format.extent8-
dc.language한국어-
dc.language.isoKOR-
dc.publisher한국정밀공학회-
dc.title스테인레스 박강판의 레이저 점 용접 시 음향방출 실시간 모니터링-
dc.title.alternativeAcoustic Emission Monitoring during Laser Spot Welding of Stainless Steel Sheets-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.bibliographicCitation한국정밀공학회지, v.22, no.4, pp 60 - 67-
dc.citation.title한국정밀공학회지-
dc.citation.volume22-
dc.citation.number4-
dc.citation.startPage60-
dc.citation.endPage67-
dc.identifier.kciidART000949498-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClasskci-
dc.subject.keywordAuthorLaser spot welding-
dc.subject.keywordAuthorWeld qualities-
dc.subject.keywordAuthorAcoustic emission monitoring-
dc.subject.keywordAuthorArtificial neural network-
dc.subject.keywordAuthor레이저 점 용접-
dc.subject.keywordAuthor용접 품질-
dc.subject.keywordAuthor음향 방출 감시-
dc.subject.keywordAuthor인공 지능 신경망-
dc.subject.keywordAuthorLaser spot welding-
dc.subject.keywordAuthorWeld qualities-
dc.subject.keywordAuthorAcoustic emission monitoring-
dc.subject.keywordAuthorArtificial neural network-
dc.identifier.urlhttps://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE00855882-
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ERICA 공학대학 (DEPARTMENT OF MECHANICAL ENGINEERING)
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