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Jung, Im Doo
Intelligent Manufacturing and Materials Lab.
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dc.citation.startPage 163828 -
dc.citation.title JOURNAL OF ALLOYS AND COMPOUNDS -
dc.citation.volume 903 -
dc.contributor.author Park, Seobin -
dc.contributor.author Kayani, Saif Haider -
dc.contributor.author Euh, Kwangjun -
dc.contributor.author Seo, Eunhyeok -
dc.contributor.author Kim, Hayeol -
dc.contributor.author Park, Sangeun -
dc.contributor.author Yadav, Bishnu Nand -
dc.contributor.author Park, Seong Jin -
dc.contributor.author Sung, Hyokyung -
dc.contributor.author Jung, Im Doo -
dc.date.accessioned 2023-12-21T14:12:59Z -
dc.date.available 2023-12-21T14:12:59Z -
dc.date.created 2022-02-11 -
dc.date.issued 2022-05 -
dc.description.abstract Here, we have approached to discover new aluminum (Al) alloys with the assistance of artificial intelligence (A.I.) for the enhanced mechanical property. A high prediction rate of 7xxx series Al alloy was achieved via the Bayesian hyperparameter optimization algorithm. With the guide of A.I.-based recommendation algorithm, new Al alloys were designed that had an excellent combination of strength and ductility with a yield strength (YS) of 712 MPa and elongation (EL) of 19%, exhibiting a homogeneous distribution of nanoscale precipitates hindering dislocation movement during deformation. Adding Mg and Cu was found to be the critical factor that decides the relative ratio of strength and EL. We also demonstrate an explainable A.I. (XAI) system that reveals the relationship between input and output parameters. Our A.I. assistant system can accelerate the search for high-strength Al alloys for both experts and non-experts in the field of Al alloy design. (c) 2022 Published by Elsevier B.V. -
dc.identifier.bibliographicCitation JOURNAL OF ALLOYS AND COMPOUNDS, v.903, pp.163828 -
dc.identifier.doi 10.1016/j.jallcom.2022.163828 -
dc.identifier.issn 0925-8388 -
dc.identifier.scopusid 2-s2.0-85123589301 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/57245 -
dc.identifier.url https://www.sciencedirect.com/science/article/pii/S0925838822002195?via%3Dihub -
dc.identifier.wosid 000749737800003 -
dc.language 영어 -
dc.publisher ELSEVIER SCIENCE SA -
dc.title High strength aluminum alloys design via explainable artificial intelligence -
dc.type Article -
dc.description.isOpenAccess FALSE -
dc.relation.journalWebOfScienceCategory Chemistry, Physical; Materials Science, Multidisciplinary; Metallurgy & Metallurgical Engineering -
dc.relation.journalResearchArea Chemistry; Materials Science; Metallurgy & Metallurgical Engineering -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor Alloy design -
dc.subject.keywordAuthor Deep neural networks -
dc.subject.keywordAuthor 7xxx aluminum alloys -
dc.subject.keywordAuthor Hyperparameter tuning -
dc.subject.keywordAuthor Explainable artificial intelligence -
dc.subject.keywordAuthor A -
dc.subject.keywordAuthor I -
dc.subject.keywordAuthor -based recommendation algorithm -
dc.subject.keywordPlus ZR-TI ALLOYS -
dc.subject.keywordPlus MG-CU ALLOY -
dc.subject.keywordPlus MICROSTRUCTURAL EVOLUTION -
dc.subject.keywordPlus PRECIPITATION EVOLUTION -
dc.subject.keywordPlus MECHANICAL-BEHAVIOR -
dc.subject.keywordPlus CORROSION BEHAVIOR -
dc.subject.keywordPlus HEAT-TREATMENT -
dc.subject.keywordPlus PROCESS MODEL -
dc.subject.keywordPlus STEEL WIRES -
dc.subject.keywordPlus AS-CAST -

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