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College of Engineering(공과대학)School of Mechanical and Aerospace Engineering(기계항공공학부)Dept. of Mechanical Engineering(기계공학과)ME-Journal Papers(저널논문)

Optimizing laser powder bed fusion of Ti-5Al-5V-5Mo-3Cr by artificial intelligence

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dc.contributor.authorShin, Da Seulko
dc.contributor.authorLee, Chi Hunko
dc.contributor.authorKuehn, Utako
dc.contributor.authorLee, Seung Chulko
dc.contributor.authorPark, Seong Jinko
dc.contributor.authorSchwab, Holgerko
dc.contributor.authorScudino, Sergioko
dc.contributor.authorKosiba, Konradko
dc.date.accessioned2023-09-13T03:01:11Z-
dc.date.available2023-09-13T03:01:11Z-
dc.date.created2023-09-13-
dc.date.created2023-09-13-
dc.date.issued2021-05-
dc.identifier.citationJOURNAL OF ALLOYS AND COMPOUNDS, v.862-
dc.identifier.issn0925-8388-
dc.identifier.urihttp://hdl.handle.net/10203/312550-
dc.description.abstractThe prerequisite for exploiting the full potential of additive manufacturing (AM) is the rapid and cost-effective fabrication of defect-free components. However, each newly processed material usually requires the identification of the optimal parameter set, a cost and time-consuming process, mostly conducted by trial and error. Here, an optimization strategy based on artificial intelligence (AI) is developed for predicting the density of additively manufactured Ti-5Al-5V-5Mo-3Cr components from experimental data. The present approach opens the way to a faster identification of the optimum set of processing parameters via AI.-
dc.languageEnglish-
dc.publisherELSEVIER SCIENCE SA-
dc.titleOptimizing laser powder bed fusion of Ti-5Al-5V-5Mo-3Cr by artificial intelligence-
dc.typeArticle-
dc.identifier.wosid000624934000018-
dc.identifier.scopusid2-s2.0-85097415652-
dc.type.rimsART-
dc.citation.volume862-
dc.citation.publicationnameJOURNAL OF ALLOYS AND COMPOUNDS-
dc.identifier.doi10.1016/j.jallcom.2020.158018-
dc.contributor.localauthorLee, Seung Chul-
dc.contributor.nonIdAuthorShin, Da Seul-
dc.contributor.nonIdAuthorLee, Chi Hun-
dc.contributor.nonIdAuthorKuehn, Uta-
dc.contributor.nonIdAuthorPark, Seong Jin-
dc.contributor.nonIdAuthorSchwab, Holger-
dc.contributor.nonIdAuthorScudino, Sergio-
dc.contributor.nonIdAuthorKosiba, Konrad-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorAdditive manufacturing-
dc.subject.keywordAuthorLaser powder bed fusion-
dc.subject.keywordAuthorTi-based alloy-
dc.subject.keywordAuthorArtificial intelligence-
dc.subject.keywordAuthorArtificial neural networks-
dc.subject.keywordAuthorDeep learning-
dc.subject.keywordPlusDEEP NEURAL-NETWORKS-
dc.subject.keywordPlusMELT POOL-
dc.subject.keywordPlusPREDICTION-
dc.subject.keywordPlusMICROSTRUCTURE-
dc.subject.keywordPlusOPTIMIZATION-
dc.subject.keywordPlusPERFORMANCE-
dc.subject.keywordPlusCHALLENGES-
dc.subject.keywordPlusFRAMEWORK-
dc.subject.keywordPlusPOROSITY-
dc.subject.keywordPlusDENSITY-
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