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정하영

Chung, Hayoung
Computational Structural Mechanics and Design Lab.
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dc.citation.endPage 820 -
dc.citation.number 4 -
dc.citation.startPage 806 -
dc.citation.title VIRTUAL AND PHYSICAL PROTOTYPING -
dc.citation.volume 17 -
dc.contributor.author Seo, Eunhyeok -
dc.contributor.author Sung, Hyokyung -
dc.contributor.author Jeon, Hongryoung -
dc.contributor.author Kim, Hayeol -
dc.contributor.author Kim, Taekyeong -
dc.contributor.author Park, Sangeun -
dc.contributor.author Lee, Min Sik -
dc.contributor.author Moon, Seung Ki -
dc.contributor.author Kim, Jung Gi -
dc.contributor.author Chung, Hayoung -
dc.contributor.author Choi, Seong-Kyum -
dc.contributor.author Yu, Ji-Hun -
dc.contributor.author Kim, Kyung Tae -
dc.contributor.author Park, Seong Jin -
dc.contributor.author Kim, Namhun -
dc.contributor.author Jung, Im Doo -
dc.date.accessioned 2023-12-21T13:39:10Z -
dc.date.available 2023-12-21T13:39:10Z -
dc.date.created 2022-05-06 -
dc.date.issued 2022-10 -
dc.description.abstract This paper proposes a novel method to impart intelligence to metal parts using additive manufacturing. A sensor-embedded metal bracket is prototyped via a metal powder bed fusion process to recognise partial screw loosening or total screw missing or identify the source of vibration with the assistance of artificial intelligence (AI). The digital metal bracket can recognise subtle changes in the screw fixation state with 90% accuracy and identify unknown sources of vibration with 84% accuracy. The von Mises stress distribution in the prototyped metal bracket is evaluated using a finite element analysis, which is learned by AI to match the real-time deformation analysis of the metal bracket in augmented reality. The proposed prototype can contribute to hyper-connectivity for developing next-generation metal-based mechanical components. -
dc.identifier.bibliographicCitation VIRTUAL AND PHYSICAL PROTOTYPING, v.17, no.4, pp.806 - 820 -
dc.identifier.doi 10.1080/17452759.2022.2068804 -
dc.identifier.issn 1745-2759 -
dc.identifier.scopusid 2-s2.0-85132648133 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/58398 -
dc.identifier.url https://www.tandfonline.com/doi/full/10.1080/17452759.2022.2068804 -
dc.identifier.wosid 000791127600001 -
dc.language 영어 -
dc.publisher TAYLOR & FRANCIS LTD -
dc.title Laser powder bed fusion for AI assisted digital metal components -
dc.type Article -
dc.description.isOpenAccess FALSE -
dc.relation.journalWebOfScienceCategory Engineering, Manufacturing;Materials Science, Multidisciplinary -
dc.relation.journalResearchArea Engineering;Materials Science -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.subject.keywordAuthor Laser powder bed fusion -
dc.subject.keywordAuthor artificial intelligence -
dc.subject.keywordAuthor sensor embedding -
dc.subject.keywordAuthor digital metal component -
dc.subject.keywordAuthor augmented reality -
dc.subject.keywordPlus PERSPECTIVES -
dc.subject.keywordPlus SENSORS -

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