Bayesian Texture Optimization using Deep Neural Network-based Numerical Material Test
Bayesian Texture Optimization using Deep Neural Network-based Numerical Material Test
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DOI：
10.1016/j.ijmecsci.2022.107285
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发表时间：
2022-04
影响因子：
7.3
通讯作者：
Ryunosuke Kamijyo;Akimitsu Ishii;S. Coppieters;A. Yamanaka
中科院分区：
文献类型：
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作者：
Ryunosuke Kamijyo;Akimitsu Ishii;S. Coppieters;A. Yamanaka
通过优化铝合金板材的晶体织构可以改善其成形性能。将联合收割机晶体塑性模拟与数学优化算法相结合的织构优化计算方法在计算上是低效的。问题的关键在于，传统的织构优化策略依赖于多个耗时的晶体塑性模拟。在本文中，我们提出了一种新的计算方法，以减轻计算工作量的数值晶体学织构优化。所提出的方法的关键点是，它实现了约三倍的显着的加速因子。首先，我们提出了一种基于深度神经网络的方法，用于基于晶体学纹理的机械性能的计算有效估计。其次，我们采用贝叶斯优化，以处理少量的试验鲁棒性和有效性。结果表明，所提出的计算方法，命名为贝叶斯纹理优化，使优选的纹理成分的最佳体积分数的确定，以获得塑性各向同性的铝合金板。此外，与传统的方法不同，贝叶斯纹理优化提供了一个框架，使一个深刻的理解的解决方案空间，可能包括其他理想的纹理和相关的不确定性。贝叶斯纹理优化为有用的工程工具铺平了道路，可以改善铝合金板材的机械性能和成形性。
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.