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Synchronously enhancing the strength, toughness, and stress corrosion resistance of high-end aluminum alloys via interpretable machine learning
Lei Jiang, Huadong Fu, Zhihao Zhang, Hongtao Zhang, Xinbiao Zhang, Xinming Feng, Xinyuan Xu, Minghong Mao 等 9 位
University of Science and Technology Beijing Liaoning Academy of Materials Beijing Laboratory of Metallic Materials and Processing for Modern Transportation
来源Acta Materialia
年份2024
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逐年被引趋势
76380
24
25
7626
关键指标
156
被引次数 · OpenAlex
27.32
领域内被引倍数
同类平均 = 1
同类平均 = 1
前 0.1%
引用位次
同领域 · 同年份 · 同类型
同领域 · 同年份 · 同类型
70
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学术脉络
学科主题
工程Aluminum Alloy Microstructure Properties
Machine Learning in Materials Science · Magnesium Alloys: Properties and Applications
参考文献 70
Stress-Corrosion Cracking of High-Strength Aluminum Alloys
被引 195Markus O. Speidel, Michael V. Hyatt · Springer eBooks · 1972
Handbook of Chemistry and Physics
被引 4,741Journal of AOAC INTERNATIONAL · 1937
Effect of iron content on fracture toughness and cracking processes in high strength AlZnMgCu alloy
被引 33Takanori Ohira, Teruo Kishi · Materials Science and Engineering · 1986
此处列出前 3 条
引用本文 156
High‐temperature ablation resistance prediction of ceramic coatings using machine learning
被引 101Jia Sun, Zhixiang Zhang, Yujia Zhang · Journal of the American Ceramic Society · 2024
Machine learning guided design and ablation behavior of ZrC-TaC-SiC ternary coatings
被引 90Yujia Zhang, Zhixiang Zhang, Xuemeng Zhang · Corrosion Science · 2025
Optimizing casting process using a combination of small data machine learning and phase-field simulations
被引 71Xiaolong Pei, Jiaqi Pei, Hua Hou · npj Computational Materials · 2025
按被引量排序,此处列出前 3 条