Machine learning discovery of a new cobalt free multi-principal-element alloy with excellent mechanical properties
In the present study, the machine learning (ML) method was utilized to construct a composition–structure–property model incorporating physical features. To enhance the predictive accuracy, the volume fraction of the two phase microstructure was merged into the dataset serving as the physical constra...
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sg-ntu-dr.10356-1620022022-09-28T08:39:58Z Machine learning discovery of a new cobalt free multi-principal-element alloy with excellent mechanical properties Qiao, Ling Ramanujan, Raju Vijayaraghavan Zhu, Jingchuan School of Materials Science and Engineering Engineering::Materials Machine Learning Multi-Principal Elements Alloy In the present study, the machine learning (ML) method was utilized to construct a composition–structure–property model incorporating physical features. To enhance the predictive accuracy, the volume fraction of the two phase microstructure was merged into the dataset serving as the physical constraint for the input variables. The physical features, the chemical composition and the temperature difference between the initial and final melting temperatures were selected as the input and output variables, respectively. To deal with the small sample data, the generalized regression neural network (GRNN) was selected and applied with optimization algorithms e.g., fruit fly optimization algorithm (FOA) and particle swarm optimization (PSO). The performance of the GRNN, FOA-GRNN and PSO-GRNN models were compared. As a result, the PSO-GRNN model was the most promising model and could be utilized to search for new multi-principal elements alloy (MPEAs) with targeted properties. Based on the ML results, a novel Fe2.5Ni2.5CrAl MPEA was designed and synthesized for experimental characterization. The DSC analysis shows that the developed alloy possesses narrower melting range and the predicted value is in excellent agreement with experiments with a relative error below 10%. The designed alloy possesses a typical dual-phase structure (FCC+BCC/B2) and exhibits exceptional mechanical properties with superior plasticity at the cast condition. This property improvement is due to solid solution strengthening and nanoparticles strengthening effects. Our proposed alloy can be a promising choice for selected high performance applications. This work is supported by AME Programmatic Fund by the Agency for Science, Technology and Research, Singapore under Grants No. A1898b0043 and A18B1b0061 and the China Scholarship Council. 2022-09-28T08:39:58Z 2022-09-28T08:39:58Z 2022 Journal Article Qiao, L., Ramanujan, R. V. & Zhu, J. (2022). Machine learning discovery of a new cobalt free multi-principal-element alloy with excellent mechanical properties. Materials Science and Engineering A, 845, 143198-. https://dx.doi.org/10.1016/j.msea.2022.143198 0921-5093 https://hdl.handle.net/10356/162002 10.1016/j.msea.2022.143198 2-s2.0-85129694600 845 143198 en A1898b0043 A18B1b0061 Materials Science and Engineering A © 2022 Elsevier B.V. All rights reserved. |
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Engineering::Materials Machine Learning Multi-Principal Elements Alloy Qiao, Ling Ramanujan, Raju Vijayaraghavan Zhu, Jingchuan Machine learning discovery of a new cobalt free multi-principal-element alloy with excellent mechanical properties |
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In the present study, the machine learning (ML) method was utilized to construct a composition–structure–property model incorporating physical features. To enhance the predictive accuracy, the volume fraction of the two phase microstructure was merged into the dataset serving as the physical constraint for the input variables. The physical features, the chemical composition and the temperature difference between the initial and final melting temperatures were selected as the input and output variables, respectively. To deal with the small sample data, the generalized regression neural network (GRNN) was selected and applied with optimization algorithms e.g., fruit fly optimization algorithm (FOA) and particle swarm optimization (PSO). The performance of the GRNN, FOA-GRNN and PSO-GRNN models were compared. As a result, the PSO-GRNN model was the most promising model and could be utilized to search for new multi-principal elements alloy (MPEAs) with targeted properties. Based on the ML results, a novel Fe2.5Ni2.5CrAl MPEA was designed and synthesized for experimental characterization. The DSC analysis shows that the developed alloy possesses narrower melting range and the predicted value is in excellent agreement with experiments with a relative error below 10%. The designed alloy possesses a typical dual-phase structure (FCC+BCC/B2) and exhibits exceptional mechanical properties with superior plasticity at the cast condition. This property improvement is due to solid solution strengthening and nanoparticles strengthening effects. Our proposed alloy can be a promising choice for selected high performance applications. |
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School of Materials Science and Engineering |
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School of Materials Science and Engineering Qiao, Ling Ramanujan, Raju Vijayaraghavan Zhu, Jingchuan |
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Article |
author |
Qiao, Ling Ramanujan, Raju Vijayaraghavan Zhu, Jingchuan |
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Qiao, Ling |
title |
Machine learning discovery of a new cobalt free multi-principal-element alloy with excellent mechanical properties |
title_short |
Machine learning discovery of a new cobalt free multi-principal-element alloy with excellent mechanical properties |
title_full |
Machine learning discovery of a new cobalt free multi-principal-element alloy with excellent mechanical properties |
title_fullStr |
Machine learning discovery of a new cobalt free multi-principal-element alloy with excellent mechanical properties |
title_full_unstemmed |
Machine learning discovery of a new cobalt free multi-principal-element alloy with excellent mechanical properties |
title_sort |
machine learning discovery of a new cobalt free multi-principal-element alloy with excellent mechanical properties |
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2022 |
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https://hdl.handle.net/10356/162002 |
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1745574669348503552 |