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Meeting 2025 TMS Annual Meeting & Exhibition
Symposium AI/Data Informatics: Computational Model Development, Verification, Validation, and Uncertainty Quantification
Presentation Title Accelerating Creep-Resistant Aluminum Alloy Design Through Generative AI-Driven Computational Models and Robust Validation
Author(s) Yizhi Wang, Yuksel Asli Sari, Mihriban Ozden Pekguleryuz
On-Site Speaker (Planned) Yizhi Wang
Abstract Scope Creep-resistant aluminum alloys are urgently needed for high-temperature structural applications for environmental benefits through lightweighting, however, conventional alloy design is unfortunately very slow. In this research, a two-stage machine-learning (ML) based approach is developed as a solution to accelerate development by discovering trends in alloy properties and creep. The first stage trains various supervised learning (SL) models on a dataset of creep-resistant alloys using composition, alloy condition (as-processed, heat treated), service conditions, and thermodynamic simulation from a hybrid ML/CALPHAD framework. The model with the highest performance in predicting creep life is identified. In the second stage, a generative genetic algorithm (GA) integrated with the SL model identifies candidate alloys for target applications. Finally, the candidates are screened through a multi-stage model-agnostic risk analysis strategy. The proposed alloys are then synthesized and tested for validation. This study emphasizes the importance of robust validation in computational materials models to ensure AI reliability.
Proceedings Inclusion? Planned:
Keywords High-Temperature Materials, Computational Materials Science & Engineering, Machine Learning

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