Discovery of ultra-high strength aluminum alloys with high damage tolerance via interpretable chain-based machine learning
This study proposes an interpretable chain-based machine learning (ICML) strategy for designing ultra-high strength aluminum alloys with high damage tolerance. Firstly, by integrating a gradient boosting regression model linking alloy composition (AC) and solution-aging processes (SAP) to tensile me...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Materials & Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127525007099 |
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| author | Lei Jiang Xinbiao Zhang Wentao Zhoutai Zhilin Han Minghong Mao Wenli Xue Jianxin Xie |
| author_facet | Lei Jiang Xinbiao Zhang Wentao Zhoutai Zhilin Han Minghong Mao Wenli Xue Jianxin Xie |
| author_sort | Lei Jiang |
| collection | DOAJ |
| description | This study proposes an interpretable chain-based machine learning (ICML) strategy for designing ultra-high strength aluminum alloys with high damage tolerance. Firstly, by integrating a gradient boosting regression model linking alloy composition (AC) and solution-aging processes (SAP) to tensile mechanical properties (TMP), including ultimate tensile strength σb, yield strength σy, and elongation A, with an explicit quantitative relationship between TMP and fatigue strength (FS), expressed as FS = ασbA1/4, a multi-scale interpretable prediction model was constructed to AC + SAP → TMP → FS. Subsequently, combining SHAP analysis, multi-objective optimization, and thermodynamic calculations, the study achieves the integrated optimization design of AC and SAP. The newly developed ultra-high strength aluminum alloy with high damage tolerance Al-10.5Zn-2.34 Mg-1.28Cu-0.11Zr-0.1Cr demonstrates outstanding mechanical properties, with a measured σb = 794 ± 2 MPa, σy = 765 ± 3 MPa, A = 11.9 ± 0.4 %, FS = 376 ± 14 MPa. Compared to commercial AA7050 and AA7055 alloys, the novel alloy demonstrated over 20 % enhancements in σb, σy, and FS, while maintaining equivalent or superior A. Furthermore, the FS = ασbA1/4 model reveals the influence of strength and plasticity on fatigue strength, enabling accurate fatigue strength predictions and excellent generalization capability, which can be extended to various metallic materials. This work provides a novel approach for efficiently designing high damage tolerance alloys. |
| format | Article |
| id | doaj-art-46196b87f0ed498ca992df3cc89e92b1 |
| institution | Kabale University |
| issn | 0264-1275 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj-art-46196b87f0ed498ca992df3cc89e92b12025-08-20T03:30:05ZengElsevierMaterials & Design0264-12752025-08-0125611428910.1016/j.matdes.2025.114289Discovery of ultra-high strength aluminum alloys with high damage tolerance via interpretable chain-based machine learningLei Jiang0Xinbiao Zhang1Wentao Zhoutai2Zhilin Han3Minghong Mao4Wenli Xue5Jianxin Xie6Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaKey Laboratory for Advanced Materials Processing (MOE), University of Science and Technology Beijing, Beijing 100083, ChinaKey Laboratory for Advanced Materials Processing (MOE), University of Science and Technology Beijing, Beijing 100083, ChinaKey Laboratory for Advanced Materials Processing (MOE), University of Science and Technology Beijing, Beijing 100083, ChinaKey Laboratory for Advanced Materials Processing (MOE), University of Science and Technology Beijing, Beijing 100083, ChinaKey Laboratory for Advanced Materials Processing (MOE), University of Science and Technology Beijing, Beijing 100083, ChinaBeijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China; Beijing Laboratory of Metallic Materials and Processing for Modern Transportation, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China; Key Laboratory for Advanced Materials Processing (MOE), University of Science and Technology Beijing, Beijing 100083, China; Institute of Materials Intelligent Technology, Liaoning Academy of Materials, Shenyang 110004, China; Corresponding author at: Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China.This study proposes an interpretable chain-based machine learning (ICML) strategy for designing ultra-high strength aluminum alloys with high damage tolerance. Firstly, by integrating a gradient boosting regression model linking alloy composition (AC) and solution-aging processes (SAP) to tensile mechanical properties (TMP), including ultimate tensile strength σb, yield strength σy, and elongation A, with an explicit quantitative relationship between TMP and fatigue strength (FS), expressed as FS = ασbA1/4, a multi-scale interpretable prediction model was constructed to AC + SAP → TMP → FS. Subsequently, combining SHAP analysis, multi-objective optimization, and thermodynamic calculations, the study achieves the integrated optimization design of AC and SAP. The newly developed ultra-high strength aluminum alloy with high damage tolerance Al-10.5Zn-2.34 Mg-1.28Cu-0.11Zr-0.1Cr demonstrates outstanding mechanical properties, with a measured σb = 794 ± 2 MPa, σy = 765 ± 3 MPa, A = 11.9 ± 0.4 %, FS = 376 ± 14 MPa. Compared to commercial AA7050 and AA7055 alloys, the novel alloy demonstrated over 20 % enhancements in σb, σy, and FS, while maintaining equivalent or superior A. Furthermore, the FS = ασbA1/4 model reveals the influence of strength and plasticity on fatigue strength, enabling accurate fatigue strength predictions and excellent generalization capability, which can be extended to various metallic materials. This work provides a novel approach for efficiently designing high damage tolerance alloys.http://www.sciencedirect.com/science/article/pii/S0264127525007099Machine learningSymbolic regressionTensile mechanical propertiesFatigue strengthAlloy design |
| spellingShingle | Lei Jiang Xinbiao Zhang Wentao Zhoutai Zhilin Han Minghong Mao Wenli Xue Jianxin Xie Discovery of ultra-high strength aluminum alloys with high damage tolerance via interpretable chain-based machine learning Materials & Design Machine learning Symbolic regression Tensile mechanical properties Fatigue strength Alloy design |
| title | Discovery of ultra-high strength aluminum alloys with high damage tolerance via interpretable chain-based machine learning |
| title_full | Discovery of ultra-high strength aluminum alloys with high damage tolerance via interpretable chain-based machine learning |
| title_fullStr | Discovery of ultra-high strength aluminum alloys with high damage tolerance via interpretable chain-based machine learning |
| title_full_unstemmed | Discovery of ultra-high strength aluminum alloys with high damage tolerance via interpretable chain-based machine learning |
| title_short | Discovery of ultra-high strength aluminum alloys with high damage tolerance via interpretable chain-based machine learning |
| title_sort | discovery of ultra high strength aluminum alloys with high damage tolerance via interpretable chain based machine learning |
| topic | Machine learning Symbolic regression Tensile mechanical properties Fatigue strength Alloy design |
| url | http://www.sciencedirect.com/science/article/pii/S0264127525007099 |
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