|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Thermodynamic Analysis for the Design of High-strength Aluminum Alloys at High Temperatures
||Takeshi Kaneshita, Shimpei Takemoto, Yoshishige Okuno, Kenji Nagata, Junya Inoue, Manabu Enoki
|On-Site Speaker (Planned)
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 150°C, 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.
||Machine Learning, High-Temperature Materials, Aluminum