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김남훈

Kim, Namhun
UNIST Computer-Integrated Manufacturing Lab.
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dc.citation.number 1 -
dc.citation.startPage e2124921 -
dc.citation.title VIRTUAL AND PHYSICAL PROTOTYPING -
dc.citation.volume 18 -
dc.contributor.author Kim, Taekyeong -
dc.contributor.author Kim, Jung Gi -
dc.contributor.author Park, Sangeun -
dc.contributor.author Kim, Hyoung Seop -
dc.contributor.author Kim, Namhun -
dc.contributor.author Ha, Hyunjong -
dc.contributor.author Choi, Seung-Kyum -
dc.contributor.author Tucker, Conrad -
dc.contributor.author Sung, Hyokyung -
dc.contributor.author Jung, Im Doo -
dc.date.accessioned 2023-12-21T13:11:04Z -
dc.date.available 2023-12-21T13:11:04Z -
dc.date.created 2022-10-21 -
dc.date.issued 2023-01 -
dc.description.abstract The core challenge in directed energy deposition is to obtain high surface quality through process optimisation, which directly affects the mechanical properties of fabricated parts. However, for expensive materials like Ti-6Al-4V, the cost and time required to optimise process parameters can be excessive in inducing good surface quality. To mitigate these challenges, we propose a novel method with artificial intelligence to generate virtual surface morphology of Ti-6Al-4V parts by given process parameters. A high-resolution surface morphology image generation system has been developed by optimising conditional generative adversarial networks. The developed virtual surface matches experimental cases well with an Frechet inception distance score of 174, in the range of accurate matching. Microstructural analysis with parts fabricated with artificial intelligence guidance exhibited less textured microstructural behaviour on the surface which reduces the anisotropy in the columnar structure. This artificial intelligence guidance of virtual surface morphology can help to obtain high-quality parts cost-effectively. -
dc.identifier.bibliographicCitation VIRTUAL AND PHYSICAL PROTOTYPING, v.18, no.1, pp.e2124921 -
dc.identifier.doi 10.1080/17452759.2022.2124921 -
dc.identifier.issn 1745-2759 -
dc.identifier.scopusid 2-s2.0-85139097379 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/59868 -
dc.identifier.wosid 000861378900001 -
dc.language 영어 -
dc.publisher TAYLOR & FRANCIS LTD -
dc.title Virtual surface morphology generation of Ti-6Al-4V directed energy deposition via conditional generative adversarial network -
dc.type Article -
dc.description.isOpenAccess TRUE -
dc.relation.journalWebOfScienceCategory Engineering, Manufacturing; Materials Science, Multidisciplinary -
dc.relation.journalResearchArea Engineering; Materials Science -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor Directed energy deposition -
dc.subject.keywordAuthor surface morphology -
dc.subject.keywordAuthor Ti-6Al-4V -
dc.subject.keywordAuthor artificial intelligence -
dc.subject.keywordAuthor conditional generative adversarial network -
dc.subject.keywordAuthor columnar structure -
dc.subject.keywordPlus DIRECT METAL-DEPOSITION -
dc.subject.keywordPlus LASER -
dc.subject.keywordPlus MICROSTRUCTURE -
dc.subject.keywordPlus STEEL -
dc.subject.keywordPlus EVOLUTION -
dc.subject.keywordPlus PARTS -

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