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Emerging microstructural characterization methods have received increased attention owing to their promise of relatively inexpensive and rapid measurement of polycrystalline surface morphology and crystallographic orientations. Among these nascent methods, polarized light microscopy (PLM) is attractive for characterizing alloys comprised of hexagonal crystals, but is hindered by its inability to measure complete crystal orientations. In this study, we explore the potential to reconstruct quasi-deterministic orientations for titanium microstructures characterized via PLM by considering the Burgers orientation relationship between the room temperature α (HCP) phase fibers measured via PLM, and the β (BCC) phase orientations of the parent grains present above the transus temperature. We describe this method—which is capable of narrowing down the orientations to one of four possibilities—and demonstrate its abilities on idealized computational samples in which the parent β microstructure is fully, unambiguously known. We further utilize this method to inform the instantiation of samples for crystal plasticity simulations, and demonstrate the significant improvement in deformation field predictions when utilizing this reconstruction method compared to using results from traditional PLM. 

Abstract Here we assess the applicability of graph neural networks (GNNs) for predicting the grain-scale elastic response of polycrystalline metallic alloys. Using GNN surrogate models, grain-averaged stresses during uniaxial elastic tension in low solvus high-refractory (LSHR) Ni Superalloy and Ti 7 wt%Al (Ti-7Al) are predicted as example face-centered cubic and hexagonal closed packed alloys, respectively. A transfer learning approach is taken in which GNN surrogate models are trained using crystal elasticity finite element method (CEFEM) simulations and then the trained surrogate models are used to predict the mechanical response of microstructures measured using high-energy X-ray diffraction microscopy (HEDM). The performance of using various microstructural and micromechanical descriptors for input nodal features to the GNNs is explored through comparisons to traditional mean-field theory predictions, reserved full-field CEFEM data, and measured far-field HEDM data. The effects of elastic anisotropy on GNN model performance and outlooks for the extension of the framework are discussed.