Reduced-Order Multiscale Modeling of Plastic Deformations in 3D Cast Metallic Alloys with Spatially Varying Microstructures

Cast aluminum alloys are increasingly utilized as light-weight materials in automobile industry due to their superior capability in withstanding high mechanical loads. A major challenge impeding the large-scale use of these alloys in high-performance applications is the presence of manufacturing-induced, spatially varying porosity defects. To understand the impacts of these defects on the macro-mechanical properties of cast alloys, multiscale simulations are often required. In this paper, we introduce a computationally efficient reduced-order multiscale framework to simulate the behavior of metallic components containing process-induced porosity under irreversible nonlinear deformations. The proposed model starts from a data compression scheme which significantly reduces the number of unknown variables by agglomerating close-by finite element nodes into a limited number of clusters. It proceeds to project solution variables into a lower dimensional space via a novel reduced-order method where the material elasto-plastic behaviors are approximated. The model then combines with a porosity-oriented microstructure characterization and reconstruction algorithm to mimic the material local heterogeneity with reconstructed pores of various morphologies and spatial distributions. The performance and versatility of the proposed approach are demonstrated by several numerical experiments.