Abstract We present a machine learning based surrogate modeling method for predicting spatially resolved 3D crystal orientation evolution of polycrystalline materials under uniaxial tensile loading. Our approach is orders of magnitude faster than the existing crystal plasticity methods enabling the simulation of large volumes that would be otherwise computationally prohibitive. This work is a major step beyond existing ML-based modeling results, which have been limited to either 2D structures or only providing average, rather than local 3D full-field predictions. We demonstrate the speed and accuracy of our surrogate model approach on experimentally collected data from a face-centered cubic copper sample undergoing tensile deformation.

中文翻译：

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基于机器学习的三维晶体变形映射代理建模方法

摘要 我们提出了一种基于机器学习的替代建模方法，用于预测单轴拉伸载荷下多晶材料的空间分辨 3D 晶体取向演变。我们的方法比现有的晶体塑性方法快几个数量级，能够模拟大体积，否则在计算上会令人望而却步。这项工作是超越现有基于 ML 的建模结果的重要一步，现有的基于 ML 的建模结果仅限于 2D 结构或仅提供平均，而不是局部 3D 全场预测。我们展示了我们的替代模型方法的速度和准确性，该方法是从经历拉伸变形的面心立方铜样品实验收集的数据。