Machine Learning–Based Reduce Order Crystal Plasticity Modeling for ICME Applications
Machine Learning–Based Reduce Order Crystal Plasticity Modeling for ICME Applications
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DOI：
10.1007/s40192-018-0123-x
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发表时间：
2018-12
影响因子：
3.3
通讯作者：
M. Yuan;S. Paradiso;B. Meredig;S. Niezgoda
中科院分区：
文献类型：
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作者：
M. Yuan;S. Paradiso;B. Meredig;S. Niezgoda
晶体塑性模拟是研究多晶材料变形过程的一种广泛应用的技术。然而，包括晶体塑性模拟到设计范例，如集成计算材料工程（ICME）的大规模模拟的计算成本的阻碍。在这项工作中，我们提出了一个机器学习（ML）框架，使用材料信息平台Open Citrination，开发和校准面心立方（FCC）多晶材料的降阶晶体塑性模型，该模型既可以快速运行，又可以轻松反转。降阶模型将晶体学织构、本构模型参数和加载条件作为输入，并返回应力-应变曲线和最终织构。该模型还可以被反演，并将应力-应变曲线、加载条件和最终纹理作为输入，并将初始纹理和本构模型参数作为输出返回。主成分分析（PCA）是用来开发一个有效的描述晶体织构。采用粘塑性自洽晶体塑性求解器，通过模拟变形过程中的应力-应变行为和织构演化来生成训练数据。
Crystal plasticity simulation is a widely used technique for studying the deformation processing of polycrystalline materials. However, inclusion of crystal plasticity simulation into design paradigms such as integrated computational materials engineering (ICME) is hindered by the computational cost of large-scale simulations. In this work, we present a machine learning (ML) framework using the material information platform, Open Citrination, to develop and calibrate a reduced order crystal plasticity model for face-centered cubic (FCC) polycrystalline materials, which can be both rapidly exercised and easily inverted. The reduced order model takes crystallographic texture, constitutive model parameters, and loading condition as inputs and returns the stress-strain curve and final texture. The model can also be inverted and take a stress-strain curve, loading condition, and final texture as inputs and return the initial texture and constitutive model parameters as outputs. Principal component analysis (PCA) is used to develop an efficient description of the crystallographic texture. A viscoplastic self-consistent (VPSC) crystal plasticity solver is used to create the training data by modeling the stress-strain behavior and evolution of texture during deformation processing.