Open Access System for Information Sharing

Login Library

 

Article
Cited 13 time in webofscience Cited 20 time in scopus
Metadata Downloads

High strength aluminum alloys design via explainable artificial intelligence SCIE SCOPUS

Title
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
Date Issued
2022-05
Publisher
Elsevier BV
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.
URI
https://oasis.postech.ac.kr/handle/2014.oak/110882
DOI
10.1016/j.jallcom.2022.163828
ISSN
0925-8388
Article Type
Article
Citation
Journal of Alloys and Compounds, vol. 903, 2022-05
Files in This Item:
There are no files associated with this item.

qr_code

  • mendeley

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher

박성진PARK, SEONG JIN
Dept of Mechanical Enginrg
Read more

Views & Downloads

Browse