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Cited 40 time in webofscience Cited 44 time in scopus
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High strength aluminum alloys design via explainable artificial intelligence

Authors
Park, SeobinKayani, Saif HaiderEuh, KwangjunSeo, EunhyeokKim, HayeolPark, SangeunYadav, Bishnu NandPark, Seong JinSung, HyokyungJung, Im Doo
Issue Date
May-2022
Publisher
Elsevier BV
Keywords
Alloy design; Deep neural networks; 7xxx aluminum alloys; Hyperparameter tuning; Explainable artificial intelligence; A; I; -based recommendation algorithm
Citation
Journal of Alloys and Compounds, v.903
Indexed
SCIE
SCOPUS
Journal Title
Journal of Alloys and Compounds
Volume
903
URI
https://scholarworks.gnu.ac.kr/handle/sw.gnu/74456
DOI
10.1016/j.jallcom.2022.163828
ISSN
0925-8388
1873-4669
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.
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공학계열 > Dept.of Materials Engineering and Convergence Technology > Journal Articles

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