An intelligent design for Ni-based superalloy based on machine learning and multi-objective optimization
[Display omitted] •A prediction model of Multi-objective Evolutionary Algorithm combined with Machine Learning was established to optimize multiple properties simultaneously.•Ni-based superalloy candidates were obtained with high γ′ solves temperature, high γ′ volume fraction and low topologically c...
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| Vydané v: | Materials & design Ročník 221; s. 110935 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.09.2022
Elsevier |
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| ISSN: | 0264-1275, 1873-4197 |
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| Abstract | [Display omitted]
•A prediction model of Multi-objective Evolutionary Algorithm combined with Machine Learning was established to optimize multiple properties simultaneously.•Ni-based superalloy candidates were obtained with high γ′ solves temperature, high γ′ volume fraction and low topologically closed-packed phase content.•Properties of Ni-based superalloy candidates verified by experiment showed great accuracy with prediction.
The simultaneous consideration of multiple conflicting properties in alloy optimization design is necessary yet remains challenging. A comprehensive strategy of machine learning (ML) and multi-objective optimization based on thermodynamic simulation data was proposed to accelerate the composition design of Ni-based superalloys. The microscopic parameters were determined by Pearson correlation analysis and domain knowledge as the key affect factors of tensile strength and elongation. The Multi-objectives Evolutionary Algorithm (MOEA) was adopted to search the well-built surrogate by ML meta-heuristically for the Pareto front of three objectives and its responding Pareto optimal solution set of composition. Furthermore, nine high-performance superalloy samples selected from the obtained Pareto front were well verified by fabricating and testing in the laboratory. Specially, a new composition among the nine as-fabricated samples was the best one according to the pre-defined design preferences with the γ′ solvus temperature, γ′ volume fraction, and TCP phases content approximating to 1210 °C, 65%, and 0.01%, respectively. This intelligent cooperation strategy based on ML and MOEA extends the methodology for multi-composition and multi-property design materials, which can optimize multiple conflicting objectives simultaneously rather than do one by one. |
|---|---|
| AbstractList | [Display omitted]
•A prediction model of Multi-objective Evolutionary Algorithm combined with Machine Learning was established to optimize multiple properties simultaneously.•Ni-based superalloy candidates were obtained with high γ′ solves temperature, high γ′ volume fraction and low topologically closed-packed phase content.•Properties of Ni-based superalloy candidates verified by experiment showed great accuracy with prediction.
The simultaneous consideration of multiple conflicting properties in alloy optimization design is necessary yet remains challenging. A comprehensive strategy of machine learning (ML) and multi-objective optimization based on thermodynamic simulation data was proposed to accelerate the composition design of Ni-based superalloys. The microscopic parameters were determined by Pearson correlation analysis and domain knowledge as the key affect factors of tensile strength and elongation. The Multi-objectives Evolutionary Algorithm (MOEA) was adopted to search the well-built surrogate by ML meta-heuristically for the Pareto front of three objectives and its responding Pareto optimal solution set of composition. Furthermore, nine high-performance superalloy samples selected from the obtained Pareto front were well verified by fabricating and testing in the laboratory. Specially, a new composition among the nine as-fabricated samples was the best one according to the pre-defined design preferences with the γ′ solvus temperature, γ′ volume fraction, and TCP phases content approximating to 1210 °C, 65%, and 0.01%, respectively. This intelligent cooperation strategy based on ML and MOEA extends the methodology for multi-composition and multi-property design materials, which can optimize multiple conflicting objectives simultaneously rather than do one by one. The simultaneous consideration of multiple conflicting properties in alloy optimization design is necessary yet remains challenging. A comprehensive strategy of machine learning (ML) and multi-objective optimization based on thermodynamic simulation data was proposed to accelerate the composition design of Ni-based superalloys. The microscopic parameters were determined by Pearson correlation analysis and domain knowledge as the key affect factors of tensile strength and elongation. The Multi-objectives Evolutionary Algorithm (MOEA) was adopted to search the well-built surrogate by ML meta-heuristically for the Pareto front of three objectives and its responding Pareto optimal solution set of composition. Furthermore, nine high-performance superalloy samples selected from the obtained Pareto front were well verified by fabricating and testing in the laboratory. Specially, a new composition among the nine as-fabricated samples was the best one according to the pre-defined design preferences with the γ′ solvus temperature, γ′ volume fraction, and TCP phases content approximating to 1210 °C, 65%, and 0.01%, respectively. This intelligent cooperation strategy based on ML and MOEA extends the methodology for multi-composition and multi-property design materials, which can optimize multiple conflicting objectives simultaneously rather than do one by one. |
| ArticleNumber | 110935 |
| Author | Wang, Yucheng Liu, Ying Zhang, Yu Lian, Lixian Deng, Yuedan Gong, Xiufang Hu, Wang |
| Author_xml | – sequence: 1 givenname: Yuedan surname: Deng fullname: Deng, Yuedan organization: College of Materials Science and Engineering, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu 610065, PR China – sequence: 2 givenname: Yu orcidid: 0000-0003-3728-3342 surname: Zhang fullname: Zhang, Yu organization: School of Computer Science and Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu 611731, PR China – sequence: 3 givenname: Xiufang surname: Gong fullname: Gong, Xiufang organization: State Key Laboratory for Long-life High Temperature Materials, 666 Jinshajing West Road, Deyang 618000, PR China – sequence: 4 givenname: Wang surname: Hu fullname: Hu, Wang organization: School of Computer Science and Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu 611731, PR China – sequence: 5 givenname: Yucheng surname: Wang fullname: Wang, Yucheng organization: College of Materials Science and Engineering, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu 610065, PR China – sequence: 6 givenname: Ying surname: Liu fullname: Liu, Ying organization: College of Materials Science and Engineering, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu 610065, PR China – sequence: 7 givenname: Lixian surname: Lian fullname: Lian, Lixian email: scu_lianyi@126.com organization: College of Materials Science and Engineering, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu 610065, PR China |
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| Keywords | Ni-based superalloys Multi-objective evolutionary algorithm Microscopic parameters Thermo-Calc Machine learning |
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•A prediction model of Multi-objective Evolutionary Algorithm combined with Machine Learning was established to optimize multiple properties... The simultaneous consideration of multiple conflicting properties in alloy optimization design is necessary yet remains challenging. A comprehensive strategy... |
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| Title | An intelligent design for Ni-based superalloy based on machine learning and multi-objective optimization |
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