Intelligent design of Fe–Cr–Ni–Al/Ti multi-principal element alloys based on machine learning
Multi-principal element alloys (MPEAs), distinguished by their complex compositions and exceptional mechanical properties, pose significant challenges for conventional predictive approaches in mechanical property optimization. This study proposes an innovative intelligent optimization algorithm (OA)...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Journal of Materials Research and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785425005666 |
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| author | Kang Xu Zhengming Sun Jian Tu Wenwang Wu Huihui Yang |
| author_facet | Kang Xu Zhengming Sun Jian Tu Wenwang Wu Huihui Yang |
| author_sort | Kang Xu |
| collection | DOAJ |
| description | Multi-principal element alloys (MPEAs), distinguished by their complex compositions and exceptional mechanical properties, pose significant challenges for conventional predictive approaches in mechanical property optimization. This study proposes an innovative intelligent optimization algorithm (OA) to refine feature selection in machine learning (ML) models, targeting the prediction of ultimate tensile strength (UTS) and fracture elongation (FE) in MPEAs. Comparative analysis with genetic algorithms (GA) reveals that the OA achieves high computational efficiency and improved prediction accuracy, demonstrating superior convergence rates and feature recognition capability. Additionally, low-cost experimental data from Fe–Cr–Ni–Al/Ti alloys are used to revise the model, thereby enhancing its prediction accuracy for high-cost processes. Validation experiments on Al5(Fe10Cr35Ni55)95 and Al2Ti1(Fe10Cr35Ni55)97 alloys yielded UTS prediction errors of 7.80% and 2.56%, with corresponding FE errors of 0.35% and 1.09%. These results confirm that strategic integration of experimental data within defined compositional ranges can substantially improve ML model performance. |
| format | Article |
| id | doaj-art-2e648758dcff4cacb15c8e5e4380d712 |
| institution | DOAJ |
| issn | 2238-7854 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj-art-2e648758dcff4cacb15c8e5e4380d7122025-08-20T02:58:51ZengElsevierJournal of Materials Research and Technology2238-78542025-03-01356864687310.1016/j.jmrt.2025.03.056Intelligent design of Fe–Cr–Ni–Al/Ti multi-principal element alloys based on machine learningKang Xu0Zhengming Sun1Jian Tu2Wenwang Wu3Huihui Yang4School of Materials Science and Engineering, Southeast University, Nanjing, 211189, China; School of Material Science and Engineering, Chongqing University of Technology, Chongqing, 400054, China; Suzhou Laboratory, No.388, Ruoshui Street, SIP, Jiangsu, 215123, ChinaSchool of Materials Science and Engineering, Southeast University, Nanjing, 211189, China; Corresponding author.School of Material Science and Engineering, Chongqing University of Technology, Chongqing, 400054, China; Corresponding author.Suzhou Laboratory, No.388, Ruoshui Street, SIP, Jiangsu, 215123, China; Corresponding author.Suzhou Laboratory, No.388, Ruoshui Street, SIP, Jiangsu, 215123, ChinaMulti-principal element alloys (MPEAs), distinguished by their complex compositions and exceptional mechanical properties, pose significant challenges for conventional predictive approaches in mechanical property optimization. This study proposes an innovative intelligent optimization algorithm (OA) to refine feature selection in machine learning (ML) models, targeting the prediction of ultimate tensile strength (UTS) and fracture elongation (FE) in MPEAs. Comparative analysis with genetic algorithms (GA) reveals that the OA achieves high computational efficiency and improved prediction accuracy, demonstrating superior convergence rates and feature recognition capability. Additionally, low-cost experimental data from Fe–Cr–Ni–Al/Ti alloys are used to revise the model, thereby enhancing its prediction accuracy for high-cost processes. Validation experiments on Al5(Fe10Cr35Ni55)95 and Al2Ti1(Fe10Cr35Ni55)97 alloys yielded UTS prediction errors of 7.80% and 2.56%, with corresponding FE errors of 0.35% and 1.09%. These results confirm that strategic integration of experimental data within defined compositional ranges can substantially improve ML model performance.http://www.sciencedirect.com/science/article/pii/S2238785425005666Alloy designMachine learningFeature selectionMechanical properties |
| spellingShingle | Kang Xu Zhengming Sun Jian Tu Wenwang Wu Huihui Yang Intelligent design of Fe–Cr–Ni–Al/Ti multi-principal element alloys based on machine learning Journal of Materials Research and Technology Alloy design Machine learning Feature selection Mechanical properties |
| title | Intelligent design of Fe–Cr–Ni–Al/Ti multi-principal element alloys based on machine learning |
| title_full | Intelligent design of Fe–Cr–Ni–Al/Ti multi-principal element alloys based on machine learning |
| title_fullStr | Intelligent design of Fe–Cr–Ni–Al/Ti multi-principal element alloys based on machine learning |
| title_full_unstemmed | Intelligent design of Fe–Cr–Ni–Al/Ti multi-principal element alloys based on machine learning |
| title_short | Intelligent design of Fe–Cr–Ni–Al/Ti multi-principal element alloys based on machine learning |
| title_sort | intelligent design of fe cr ni al ti multi principal element alloys based on machine learning |
| topic | Alloy design Machine learning Feature selection Mechanical properties |
| url | http://www.sciencedirect.com/science/article/pii/S2238785425005666 |
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