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Jung, Im Doo
Intelligent Manufacturing and Materials Lab.
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Embedding sensors using selective laser melting for self-cognitive metal parts

Author(s)
Jung, Im DooLee, Min SikLee, JungsubSung, HyokyungChoe, JunghoSon, Hye JinYun, JaecheolKim, Ki-bongKim, MoobumLee, Seok WooYang, SangsunMoon, Seung KiKim, Kyung TaeYu, Ji-Hun
Issued Date
2020-05
DOI
10.1016/j.addma.2020.101151
URI
https://scholarworks.unist.ac.kr/handle/201301/48353
Fulltext
https://www.sciencedirect.com/science/article/pii/S2214860419313065
Citation
ADDITIVE MANUFACTURING, v.33, pp.101151
Abstract
We 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.
Publisher
ELSEVIER
ISSN
2214-8604
Keyword (Author)
Self-cognitive metal partInternet of thingsSelective laser meltingSensor embeddingHyper connection
Keyword
MECHANICAL-PROPERTIESLATTICE STRUCTURESMATRIX COMPOSITESINTERNETTHINGSMICROSTRUCTURESTRENGTHSLMOPTIMIZATIONPROPERTY

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