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Meeting 2022 TMS Annual Meeting & Exhibition
Symposium AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
Presentation Title Thermodynamic Analysis for the Design of High-strength Aluminum Alloys at High Temperatures
Author(s) Takeshi Kaneshita, Shimpei Takemoto, Yoshishige Okuno, Kenji Nagata, Junya Inoue, Manabu Enoki
On-Site Speaker (Planned) Shimpei Takemoto
Abstract Scope We discuss the design of 2000 series high-strength aluminum alloys at high temperatures using Bayesian learning for neural networks and thermodynamic analysis. It is known that the strength of aluminum alloys decreases rapidly above 150C, so improving the strength at high temperatures is essential for industrial applications. In order to design high-strength alloys, it is necessary to optimize the additive element compositions and the heat treatment conditions such as temperature and time for homogenization, solution processing, and aging. A data science approach using neural networks is suitable for handling such multi-dimensional problems and exploring the optimal process conditions from the vast design space. This study focuses on the thermodynamic calculations, including the CALPHAD method, the Langer-Schwartz-Kampmann-Wagner approach, and the phase-field method for analyzing the strengthening mechanism of the alloy designs suggested by the neural network.
Proceedings Inclusion? Planned:
Keywords Machine Learning, High-Temperature Materials, Aluminum

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