About this Abstract |
Meeting |
2021 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data informatics: Design of Structural Materials
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Presentation Title |
Prediction of the Mechanical Properties of Aluminum Alloy Using Bayesian Learning for Neural Networks |
Author(s) |
Shimpei Takemoto, Kenji Nagata, Takeshi Kaneshita, Yoshishige Okuno, Katsuki Okuno, Masamichi Kitano, Junya Inoue, Manabu Enoki |
On-Site Speaker (Planned) |
Shimpei Takemoto |
Abstract Scope |
Understanding the process-structure-property relationship is one of the goals of computational materials design. In this study, we analyze the strengthening mechanism of 2000 series aluminum alloy using neural networks. We have constructed a neural network for the simultaneous prediction of multiple mechanical properties, including ultimate tensile strength, tensile yield strength, and elongation. Replica-exchange Monte Carlo method, an extended Markov chain Monte Carlo (MCMC) method, has been applied for the Bayesian estimation of the optimal neural network architecture and hyperparameters. The obtained neural network, combined with the thermodynamic analysis using the CALPHAD method implemented in the Thermo-Calc software, enables us to identify a dominant combination of additive elements and thermal processing for strengthening alloys. We have also addressed an inverse problem for optimizing the process parameters for a set of desired properties. The approach we propose will accelerate the design of high strength alloys for high-temperature applications. |
Proceedings Inclusion? |
Planned: |