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Cited 2 time in webofscience Cited 2 time in scopus
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Direct energy deposition for smart micro reactor

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dc.contributor.authorKim, Hayeol-
dc.contributor.authorSeo, Junyoung-
dc.contributor.authorBaek, Adrian Matias Chung-
dc.contributor.authorShin, Woo Yeong-
dc.contributor.authorJeon, Hongryung-
dc.contributor.authorMoon, Seung Ki-
dc.contributor.authorKim, Hyungmo-
dc.contributor.authorKim, Namhun-
dc.contributor.authorJung, Im Doo-
dc.date.accessioned2024-12-03T06:00:43Z-
dc.date.available2024-12-03T06:00:43Z-
dc.date.issued2024-12-
dc.identifier.issn1745-2759-
dc.identifier.issn1745-2767-
dc.identifier.urihttps://scholarworks.gnu.ac.kr/handle/sw.gnu/74444-
dc.description.abstractNuclear microreactors (MRs) have attracted significant attention for their versatility in installation locations, especially with the growing demand for sustainable green energy in large data processing facilities. However, ensuring their safe operation necessitates frequent inspections, which can be challenging for practical efficient management. This study proposes a novel approach using direct energy deposition to incorporate an optical fiber sensor into MR components, enabling real-time monitoring with artificial intelligence. The embedded optical fiber generates real-time data that allows for AI-driven in-vivo thermal deformation analysis. Our smart MR component can detect structural anomalies, identify abnormal operations, and assess operational conditions through augmented reality interfaces and AI technology.-
dc.language영어-
dc.language.isoENG-
dc.publisherTaylor & Francis-
dc.titleDirect energy deposition for smart micro reactor-
dc.typeArticle-
dc.publisher.location영국-
dc.identifier.doi10.1080/17452759.2024.2411024-
dc.identifier.scopusid2-s2.0-85206366941-
dc.identifier.wosid001330052000001-
dc.identifier.bibliographicCitationVirtual and Physical Prototyping, v.19, no.1-
dc.citation.titleVirtual and Physical Prototyping-
dc.citation.volume19-
dc.citation.number1-
dc.type.docTypeArticle-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaMaterials Science-
dc.relation.journalWebOfScienceCategoryEngineering, Manufacturing-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.subject.keywordPlusNUCLEAR-
dc.subject.keywordPlusPERFORMANCE-
dc.subject.keywordPlusSENSORS-
dc.subject.keywordPlusSTRESS-
dc.subject.keywordPlusFIBERS-
dc.subject.keywordAuthor3-Dimensional printing-
dc.subject.keywordAuthordirect metal deposition-
dc.subject.keywordAuthormachine learning-
dc.subject.keywordAuthordesign for additive manufacturing-
dc.subject.keywordAuthorvirtual prototyping-
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