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 the $$\gamma -\gamma '$$ intragranular 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.

中文翻译：

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用于单晶镍基高温合金的机器学习辅助参数均质化晶体塑性模型 (PHCPM)

本文为单晶镍基高温合金的参数均质化晶体塑性模型 (PHCPM) 建立了多尺度建模框架。PHCPM 明确地将 $$\gamma -\gamma '$$ 晶内微观结构的形态统计数据纳入其晶体可塑性本构系数。它们为基于图像的多晶微结构模拟提供高效和准确的计算。单晶 PHCPM 开发过程包括：(1) 构建统计等效的 RVE 或 SERVE，(2) 基于图像的位错密度晶体塑性模型建模，(3) 识别代表性聚合微观结构参数，(4) 选择PHCPM 框架和 (5) 自洽的同质化。在每个开发阶段都会探索新的机器学习工具。有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 均值和符号回归、增强优化、模型仿真和灵敏度分析方法，都是多尺度建模管道的关键组成部分。机器学习工具与基于物理的模型的集成能够为多晶模拟创建强大的单晶本构模型。