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Bayesian texture optimization using deep neural network-based numerical material test
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DOI：10.1016/j.ijmecsci.2022.107285.png)
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The formability of an aluminum alloy sheet can be improved by optimizing its crystallographic texture. Computational methods for texture optimization that combine crystal plasticity simulations with mathematical optimization algorithms are computationally inefficient. The crux of the problem is that conventional texture optimization strategies rely on multiple time-consuming crystal plasticity simulations. In this paper, we propose a new computational method for mitigating computational effort in numerical crystallographic texture optimization. The key point of the proposed method is that it achieves a significant speed-up factor of approximately three-fold. First, we propose a deep neural network-based approach for the computationally efficient estimation of mechanical properties based on the crystallographic texture. Second, we adopted Bayesian optimization to deal with a small number of trials robustly and efficiently. It is shown that the proposed computational method, christened Bayesian texture optimization, enables the determination of optimal volume fractions of preferred texture components to obtain a plastically isotropic aluminum alloy sheet. Moreover, unlike conventional methods, Bayesian texture optimization provides a framework that enables a profound understanding of the solution space that may consist of other desirable textures and associated uncertainties. Bayesian texture optimization paves the way for useful engineering tools that can improve the mechanical properties and formability of aluminum alloy sheets.
Keyword:
Deep learning
Bayesian optimization
Lankford value
Crystal plasticity
Crystallographic texture
期刊
IF:
9.4
论文数:
1.0W
被引数:
4.5W
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