arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.00951cond-mat.mtrl-sciphysics.comp-ph
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基于机器学习的代理晶体塑性模型在单轴拉伸下的空间分辨三维取向演化

Machine learning enabled surrogate crystal plasticity model for spatially resolved 3D orientation evolution under uniaxial tension

Anup Pandey, Reeju Pokharel

摘要:

提出了一种新的基于机器学习的代理建模方法,用于预测多晶材料在单轴拉伸载荷下的空间分辨三维微观结构演化。我们的方法比现有的晶体塑性方法快几个数量级,从而能够模拟大体积,否则计算上是禁止的。这项工作是超越现有的基于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的预测。我们证明了我们的代理模型方法的速度和准确性的实验测量的微观结构从高能X射线衍射显微镜的面心立方铜样品,进行拉伸变形。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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