Machine Learning-Aided Parametrically Homogenized Crystal Plasticity Model (PHCPM) for Single Crystal Ni-Based Superalloys
Machine Learning-Aided Parametrically Homogenized Crystal Plasticity Model (PHCPM) for Single Crystal Ni-Based Superalloys
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DOI：
10.1007/s11837-020-04344-9
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发表时间：
2020-09
期刊：
影响因子：
2.6
通讯作者：
G. Weber;M. Pinz;Somnath Ghosh
中科院分区：
文献类型：
--
作者：
G. Weber;M. Pinz;Somnath Ghosh
本文建立了单晶镍基高温合金参数均质晶体塑性模型（PHCPM）的多尺度建模框架。 PHCPM 明确地将晶内微观结构的形态统计纳入其晶体塑性本构系数中。它们能够为基于图像的多晶微结构模拟进行高效、准确的计算。单晶 PHCPM 开发过程包括：(1) 构建统计上等效的 RVE 或 SERVE，(2) 使用位错密度晶体塑性模型进行基于图像的建模，(3) 识别代表性聚合微观结构参数，(4) 选择 PHCPM 框架，以及 (5) 自洽均质化。在每个开发阶段都会探索新颖的机器学习工具。监督和无监督学习方法，例如支持向量回归、人工神经网络、k-means 和符号回归、增强优化、模型仿真和敏感性分析方法都是多尺度建模流程的关键组成部分。机器学习工具与基于物理的模型的集成使得能够为多晶模拟创建强大的单晶本构模型。
This article establishes a multiscale modeling framework for the parametrically homogenized crystal plasticity model (PHCPM) for single crystal Ni-based superalloys. The PHCPMs explicitly incorporate morphological statistics of theintragranular microstructure in their crystal plasticity constitutive coefficients. They enable highly efficient and accurate calculations for image-based polycrystalline microstructural simulations. The single crystal PHCPM development process involves: (1) construction of statistically equivalent RVEs or SERVEs, (2) image-based modeling with a dislocation-density crystal plasticity model, (3) identification of representative aggregated microstructural parameters, (4) selection of a PHCPM framework and (5) self-consistent homogenization. Novel machine learning tools are explored at every development phase. Supervised and unsupervised learning methods, such as support vector regression, artificial neural networks,k-means, and symbolic regression, enhanced optimization, model emulation and sensitivity analysis methods are all critical components of the multiscale modeling pipeline. The integration of machine learning tools with physics-based models enables the creation of powerful single crystal constitutive models for polycrystalline simulations.