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Cited 24 time in webofscience Cited 22 time in scopus
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Embedding sensors using selective laser melting for self-cognitive metal parts

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dc.contributor.authorJung, Im Doo-
dc.contributor.authorLee, Min Sik-
dc.contributor.authorLee, Jungsub-
dc.contributor.authorSung, Hyokyung-
dc.contributor.authorChoe, Jungho-
dc.contributor.authorSon, Hye Jin-
dc.contributor.authorYun, Jaecheol-
dc.contributor.authorKim, Ki-bong-
dc.contributor.authorKim, Moobum-
dc.contributor.authorLee, Seok Woo-
dc.contributor.authorYang, Sangsun-
dc.contributor.authorMoon, Seung Ki-
dc.contributor.authorKim, Kyung Tae-
dc.contributor.authorYu, Ji-Hun-
dc.date.accessioned2024-12-02T21:31:06Z-
dc.date.available2024-12-02T21:31:06Z-
dc.date.issued2020-05-
dc.identifier.issn2214-8604-
dc.identifier.issn2214-7810-
dc.identifier.urihttps://scholarworks.gnu.ac.kr/handle/sw.gnu/72030-
dc.description.abstractWe devised a novel method to embed sensors or integrated circuit (IC) chips into metal components by using a selective laser melting (SLM) process. The concept of a protective layer is introduced to fabricate all parts without damaging the sensors during the laser scanning process. The operation of sensors in the parts is analyzed from a computational analysis on the thermal influence of laser heat. The fabricated metal parts show continuous microstructures including grains and phases between the base part and the new part formed after embedding the sensor despite the intermittent SLM process. The embedded sensor operates properly when compared to bare sensors. Plastic circuit board-based IC components were embedded into an Inconel 718C turbine blade, which accurately distinguished three-dimensional vibration along the X, Y, and Z axes. Our results imply that the proposed process can open new avenues for SLM technology to realize metal components with a self-cognitive ability using integrated sensors.-
dc.language영어-
dc.language.isoENG-
dc.publisherElsevier BV-
dc.titleEmbedding sensors using selective laser melting for self-cognitive metal parts-
dc.typeArticle-
dc.publisher.location네델란드-
dc.identifier.doi10.1016/j.addma.2020.101151-
dc.identifier.scopusid2-s2.0-85081019564-
dc.identifier.wosid000539268800005-
dc.identifier.bibliographicCitationAdditive Manufacturing, v.33-
dc.citation.titleAdditive Manufacturing-
dc.citation.volume33-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaMaterials Science-
dc.relation.journalWebOfScienceCategoryEngineering, Manufacturing-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.subject.keywordPlusMECHANICAL-PROPERTIES-
dc.subject.keywordPlusLATTICE STRUCTURES-
dc.subject.keywordPlusMATRIX COMPOSITES-
dc.subject.keywordPlus316L PARTS-
dc.subject.keywordPlusINTERNET-
dc.subject.keywordPlusTHINGS-
dc.subject.keywordPlusSLM-
dc.subject.keywordPlusMICROSTRUCTURE-
dc.subject.keywordPlusSTRENGTH-
dc.subject.keywordPlusOPTIMIZATION-
dc.subject.keywordAuthorSelf-cognitive metal part-
dc.subject.keywordAuthorInternet of things-
dc.subject.keywordAuthorSelective laser melting-
dc.subject.keywordAuthorSensor embedding-
dc.subject.keywordAuthorHyper connection-
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