Active learning-based alloy design strategy for improving the strength-ductility balance of Al-Mg-Zn alloys
Al-Mg-Zn alloys, designed to combine the formability of 5xxx alloys with the high strength of 7xxx alloys, still face challenges in achieving an optimal strength-ductility balance. This study presents an active learning-based alloy design strategy to guide experiments aimed at enhancing the strength...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Materials & Design |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127525001923 |
| _version_ | 1826552723879755776 |
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| author | Wuwei Mo Yao Xiao Yushen Huang Peng Sun Ya Li Xiaoyu Zheng Qiang Lu Bo Li Yuling Liu Yong Du |
| author_facet | Wuwei Mo Yao Xiao Yushen Huang Peng Sun Ya Li Xiaoyu Zheng Qiang Lu Bo Li Yuling Liu Yong Du |
| author_sort | Wuwei Mo |
| collection | DOAJ |
| description | Al-Mg-Zn alloys, designed to combine the formability of 5xxx alloys with the high strength of 7xxx alloys, still face challenges in achieving an optimal strength-ductility balance. This study presents an active learning-based alloy design strategy to guide experiments aimed at enhancing the strength-ductility balance in Al-Mg-Zn alloys. Firstly, a sub-dataset comprising ultimate tensile strength (UTS) and elongation (EL) data with optimal generalization ability was identified from the small and disordered Al-Mg-Zn dataset using the bagging method. Subsequently, the bagging model of this sub-dataset was employed to construct a Pareto front based on the Upper Confidence Bound for UTS and EL, providing guidance for alloy composition design. Through experimental validation and iterative optimization, the strength-ductility balance of Al-Mg-Zn alloys was significantly improved, with the designed Al-5.27Mg-2.8Zn-0.44Cu-0.19Ag-0.15Sc-0.05Mn-0.01Zr alloy (wt.%) exhibiting superior mechanical properties with the measured UTS of 602 MPa and EL of 15.1 %. Microstructural analysis using SEM, EBSD and TEM revealed that the improved strength-ductility balance of the alloy is attributed to its optimized composition, which results in the minimal micron phases, numerous fine Al3Sc particles, low-recrystallization grains, and a high density of precipitates. This active learning-based design strategy offering a novel approach for material development in systems with limited data. |
| first_indexed | 2025-03-14T07:13:40Z |
| format | Article |
| id | doaj.art-50814bdd80c24b958c802c22ce63b588 |
| institution | Directory Open Access Journal |
| issn | 0264-1275 |
| language | English |
| last_indexed | 2025-03-14T07:13:40Z |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj.art-50814bdd80c24b958c802c22ce63b5882025-03-04T04:22:25ZengElsevierMaterials & Design0264-12752025-04-01252113772Active learning-based alloy design strategy for improving the strength-ductility balance of Al-Mg-Zn alloysWuwei Mo0Yao Xiao1Yushen Huang2Peng Sun3Ya Li4Xiaoyu Zheng5Qiang Lu6Bo Li7Yuling Liu8Yong Du9State Key Laboratory of Powder Metallurgy, Central South University, Changsha, Hunan 410083, ChinaState Key Laboratory of Powder Metallurgy, Central South University, Changsha, Hunan 410083, ChinaState Key Laboratory of Powder Metallurgy, Central South University, Changsha, Hunan 410083, ChinaState Key Laboratory of Powder Metallurgy, Central South University, Changsha, Hunan 410083, ChinaState Key Laboratory of Powder Metallurgy, Central South University, Changsha, Hunan 410083, ChinaSchool of Metallurgy and Environment, Central South University, Changsha, Hunan 410083, ChinaState Key Laboratory of Powder Metallurgy, Central South University, Changsha, Hunan 410083, ChinaHubei Engineering Research Center for Graphite Additive Manufacturing Technology and Equipment, China Three Gorges University, Yichang, Hubei 443002, ChinaState Key Laboratory of Powder Metallurgy, Central South University, Changsha, Hunan 410083, China; Corresponding authors.State Key Laboratory of Powder Metallurgy, Central South University, Changsha, Hunan 410083, China; Corresponding authors.Al-Mg-Zn alloys, designed to combine the formability of 5xxx alloys with the high strength of 7xxx alloys, still face challenges in achieving an optimal strength-ductility balance. This study presents an active learning-based alloy design strategy to guide experiments aimed at enhancing the strength-ductility balance in Al-Mg-Zn alloys. Firstly, a sub-dataset comprising ultimate tensile strength (UTS) and elongation (EL) data with optimal generalization ability was identified from the small and disordered Al-Mg-Zn dataset using the bagging method. Subsequently, the bagging model of this sub-dataset was employed to construct a Pareto front based on the Upper Confidence Bound for UTS and EL, providing guidance for alloy composition design. Through experimental validation and iterative optimization, the strength-ductility balance of Al-Mg-Zn alloys was significantly improved, with the designed Al-5.27Mg-2.8Zn-0.44Cu-0.19Ag-0.15Sc-0.05Mn-0.01Zr alloy (wt.%) exhibiting superior mechanical properties with the measured UTS of 602 MPa and EL of 15.1 %. Microstructural analysis using SEM, EBSD and TEM revealed that the improved strength-ductility balance of the alloy is attributed to its optimized composition, which results in the minimal micron phases, numerous fine Al3Sc particles, low-recrystallization grains, and a high density of precipitates. This active learning-based design strategy offering a novel approach for material development in systems with limited data.http://www.sciencedirect.com/science/article/pii/S0264127525001923Aluminum alloyMachine learningActive learningAlloy designMechanical property |
| spellingShingle | Wuwei Mo Yao Xiao Yushen Huang Peng Sun Ya Li Xiaoyu Zheng Qiang Lu Bo Li Yuling Liu Yong Du Active learning-based alloy design strategy for improving the strength-ductility balance of Al-Mg-Zn alloys Materials & Design Aluminum alloy Machine learning Active learning Alloy design Mechanical property |
| title | Active learning-based alloy design strategy for improving the strength-ductility balance of Al-Mg-Zn alloys |
| title_full | Active learning-based alloy design strategy for improving the strength-ductility balance of Al-Mg-Zn alloys |
| title_fullStr | Active learning-based alloy design strategy for improving the strength-ductility balance of Al-Mg-Zn alloys |
| title_full_unstemmed | Active learning-based alloy design strategy for improving the strength-ductility balance of Al-Mg-Zn alloys |
| title_short | Active learning-based alloy design strategy for improving the strength-ductility balance of Al-Mg-Zn alloys |
| title_sort | active learning based alloy design strategy for improving the strength ductility balance of al mg zn alloys |
| topic | Aluminum alloy Machine learning Active learning Alloy design Mechanical property |
| url | http://www.sciencedirect.com/science/article/pii/S0264127525001923 |
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