Details
| Original language | English |
|---|---|
| Article number | 112612 |
| Journal | Computational materials science |
| Volume | 231 |
| Early online date | 31 Oct 2023 |
| Publication status | Published - 5 Jan 2024 |
Abstract
This paper details an alloy design effort by machine learning (ML) attempting to design novel refractory high entropy alloys (RHEAs) with exceptional mechanical properties at elevated temperatures and good room temperature ductility. For this purpose, four datasets were generated by mining the data available in literature, containing room temperature strength, high temperature strength, room temperature ductility and hardness, which were trained by three different ML models, namely the support vector regression, random forest, and artificial neural network. As a result, three novel RHEA compositions were predicted, and their performances were experimentally validated. Specifically, the Ti8Nb21Zr27Ta13Mo19V12, Ti10Nb19Zr15Ta43Mo7V6, and Ti10Nb20Zr37Mo21V12 RHEAs were produced and subjected to room-temperature and high-temperature compression, and room-temperature hardness tests, which have demonstrated that especially the Ti8Nb21Zr27Ta13Mo19V12 and the Ti10Nb20Zr37Mo21V12 RHEAs exhibit both high strength at elevated temperatures and good room-temperature ductility. The current study not only contributes to the literature by presenting three novel RHEAs, but also constitutes an encouraging example of efficient alloy design by ML for demanding applications.
Keywords
- Alloy design, Ductility, High-temperature strength, Machine learning, Refractory high entropy alloy
ASJC Scopus subject areas
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In: Computational materials science, Vol. 231, 112612, 05.01.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Machine learning assisted design of novel refractory high entropy alloys with enhanced mechanical properties
AU - Catal, A. A.
AU - Bedir, E.
AU - Yilmaz, R.
AU - Swider, M. A.
AU - Lee, C.
AU - El-Atwani, O.
AU - Maier, H. J.
AU - Ozdemir, H. C.
AU - Canadinc, D.
N1 - Funding Information: D. Canadinc acknowledges the support by the Alexander von Humboldt Foundation (Germany) within the scope of Humboldt Research Award. H.J. Maier acknowledges financial support by Deutsche Forschungsgemeinschaft ( project #388671975 ) (Germany).
PY - 2024/1/5
Y1 - 2024/1/5
N2 - This paper details an alloy design effort by machine learning (ML) attempting to design novel refractory high entropy alloys (RHEAs) with exceptional mechanical properties at elevated temperatures and good room temperature ductility. For this purpose, four datasets were generated by mining the data available in literature, containing room temperature strength, high temperature strength, room temperature ductility and hardness, which were trained by three different ML models, namely the support vector regression, random forest, and artificial neural network. As a result, three novel RHEA compositions were predicted, and their performances were experimentally validated. Specifically, the Ti8Nb21Zr27Ta13Mo19V12, Ti10Nb19Zr15Ta43Mo7V6, and Ti10Nb20Zr37Mo21V12 RHEAs were produced and subjected to room-temperature and high-temperature compression, and room-temperature hardness tests, which have demonstrated that especially the Ti8Nb21Zr27Ta13Mo19V12 and the Ti10Nb20Zr37Mo21V12 RHEAs exhibit both high strength at elevated temperatures and good room-temperature ductility. The current study not only contributes to the literature by presenting three novel RHEAs, but also constitutes an encouraging example of efficient alloy design by ML for demanding applications.
AB - This paper details an alloy design effort by machine learning (ML) attempting to design novel refractory high entropy alloys (RHEAs) with exceptional mechanical properties at elevated temperatures and good room temperature ductility. For this purpose, four datasets were generated by mining the data available in literature, containing room temperature strength, high temperature strength, room temperature ductility and hardness, which were trained by three different ML models, namely the support vector regression, random forest, and artificial neural network. As a result, three novel RHEA compositions were predicted, and their performances were experimentally validated. Specifically, the Ti8Nb21Zr27Ta13Mo19V12, Ti10Nb19Zr15Ta43Mo7V6, and Ti10Nb20Zr37Mo21V12 RHEAs were produced and subjected to room-temperature and high-temperature compression, and room-temperature hardness tests, which have demonstrated that especially the Ti8Nb21Zr27Ta13Mo19V12 and the Ti10Nb20Zr37Mo21V12 RHEAs exhibit both high strength at elevated temperatures and good room-temperature ductility. The current study not only contributes to the literature by presenting three novel RHEAs, but also constitutes an encouraging example of efficient alloy design by ML for demanding applications.
KW - Alloy design
KW - Ductility
KW - High-temperature strength
KW - Machine learning
KW - Refractory high entropy alloy
UR - http://www.scopus.com/inward/record.url?scp=85175016758&partnerID=8YFLogxK
U2 - 10.1016/j.commatsci.2023.112612
DO - 10.1016/j.commatsci.2023.112612
M3 - Article
AN - SCOPUS:85175016758
VL - 231
JO - Computational materials science
JF - Computational materials science
SN - 0927-0256
M1 - 112612
ER -