Microstructure and property based statistically equivalent RVEs for polycrystalline-polyphase aluminum alloys

Abstract

This paper develops necessary preprocessors for image-based micromechanical analysis of polycrystalline-polyphase microstructures of Al alloys such as Al7075-T651. Starting from input data in the form of electron back scatter diffraction (EBSD) and scanning electron microscopy (SEM) maps of orthogonal surfaces of experimental specimens, the paper creates a robust methodology for generating 3D statistically equivalent virtual microstructures (3D-SEVMs) by 3D stereological projection of 2D statistical distribution and correlation functions using a genetic algorithm (GA)-based numerical algorithm. Validation studies of the SEVM reconstruction process by comparing with morphological and crystallographic distributions of grains and precipitates from the experiments. Next, microstructure-based statistically equivalent representative volume element (M-SERVE) that corresponds to the minimum sized SERVE for convergence of morphological or crystallographic distributions, are established using the Kolmogorov–Smirnov (KS) tests. Finally, property-based statistically equivalent RVE (P-SERVE), defined as the smallest SERVE for predicting response functions (both effective and local), are estimated by conducting crystal plasticity finite-element simulations. Convergence plots of material response functions are used to assess the P-SERVE. Comparison of M-SERVE and P-SERVE sizes are made to comprehend microstructure-property relations.