论文标题
机器学习启用了在单轴张力下的空间解决3D方向演变的替代晶体可塑性模型
Machine learning enabled surrogate crystal plasticity model for spatially resolved 3D orientation evolution under uniaxial tension
论文作者
论文摘要
我们提出了一种基于机器学习的新型替代建模方法，用于预测单轴拉伸负荷下多晶材料的空间分辨的3D微结构演化。我们的方法是比现有的晶体可塑性方法快的数量级，该方法可以模拟大量的大容量。这项工作是超出现有基于ML的建模结果的重要一步，该结果仅限于2D结构，或者仅提供平均值而不是本地预测。我们证明了我们的替代模型方法在实验测量的微观结构中，从面部为中心的立方铜样品的高能量X射线衍射显微镜进行实验测量的微观结构的速度和准确性。
We present a novel machine learning based surrogate modeling method for predicting spatially resolved 3D microstructure 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, predictions. We demonstrate the speed and accuracy of our surrogate model approach on experimentally measured microstructure from high-energy X-ray diffraction microscopy of a face-centered cubic copper sample, undergoing tensile deformation.
