Machine Learning-Aided Parametrically Homogenized Crystal Plasticity Model (PHCPM) for Single Crystal Ni-Based Superalloys

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 γ-γ′\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma -\gamma '$$\end{document} 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.