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Predicting the optimum compositions of high-performance Cu–Zn alloys via machine learning

Published online by Cambridge University Press:  21 September 2020

Baobin Xie ,
Qihong Fang ,
Jia Li Open the ORCID record for Jia Li [Opens in a new window]  and
Peter K. Liaw
Baobin Xie
Affiliation:
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha410082, PR China
Qihong Fang*
Affiliation:
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha410082, PR China
Jia Li*
Affiliation:
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha410082, PR China
Peter K. Liaw
Affiliation:
Department of Materials Science and Engineering, The University of Tennessee, Knoxville, Tennessee37996, USA
*
a)Address all correspondence to these authors. e-mail: [email protected]
b)e-mail: [email protected]
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Abstract

In the alloy materials, their mechanical properties mightly rely on the compositions and concentrations of chemical elements. Therefore, looking for the optimum elemental concentration and composition is still a critical issue to design high-performance alloy materials. Traditional alloy designing method via “trial and error” or domain experts’ experiences is barely possible to solve the issue. Here, we propose a “composition-oriented” method combined machine learning to design the Cu–Zn alloys with the high strengths, high ductility, and low friction coefficient. The method of separate training for each attribute label is used to study the effects of elemental concentrations on the mechanical properties of Cu–Zn alloys. Moreover, the elemental concentrations of new Cu–Zn alloys with the good mechanical properties are predicted by machine learning. The current results reveal the vital importance of the “composition-oriented” design method via machine learning for the development of high-performance alloys in a broad range of elemental compositions.

Keywords

copper alloyoptimum elemental compositionbest mechanical propertiesmachine learning
Type
Article
Information
Journal of Materials Research , Volume 35 , Issue 20 , 28 October 2020 , pp. 2709 - 2717
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press on behalf of Materials Research Society

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