Artificial intelligence-enabled synergistic design of strength and stress corrosion cracking resistance in light alloys

Yakun Zhu; Lu Zhang; Rui Yang; Yixuan Wang; Weidong Li; Luning Wang

doi:10.1002/mgea.70039

Materials Genome Engineering Advances ›› 2025, Vol. 3 ›› Issue (4) :e70039 DOI: 10.1002/mgea.70039

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Artificial intelligence-enabled synergistic design of strength and stress corrosion cracking resistance in light alloys

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Abstract

High-performance light alloys, including aluminum, titanium, magnesium alloys, etc., are utilized in aerospace, aviation, transportation and medical applications. A key challenge for these alloys is achieving both improved strength and stress corrosion cracking (SCC) resistance by optimizing the relationships between composition, processing, microstructure, and macroscopic properties. Artificial intelligence (AI)-driven multi-modal machine learning offers opportunities for materials design and prediction. Proposed strategies include applying machine learning-based approaches for concurrent improvement of alloy strength and SCC resistance, conducting in situ high-throughput experiments to investigate SCC microcrack initiation mechanisms under combined mechanical, microstructural, and corrosion conditions to support database development and developing correlative AI models for alloy microstructure evolution and macroscopic SCC failure behavior in service environments.

Keywords

concurrent design strategy / multi-modal machine learning / strength / stress corrosion cracking

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Yakun Zhu, Lu Zhang, Rui Yang, Yixuan Wang, Weidong Li, Luning Wang. Artificial intelligence-enabled synergistic design of strength and stress corrosion cracking resistance in light alloys. Materials Genome Engineering Advances, 2025, 3(4): e70039 DOI:10.1002/mgea.70039

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