  Aluminum alloys are increasingly utilized as lightweight materials in the
automobile industry due to their superior capability in withstanding high
mechanical loads. A significant challenge impeding the large-scale use of these
alloys in high-performance applications is the presence of
manufacturing-induced, spatially varying porosity defects. In order 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. In our approach, we start
with a data compression scheme that significantly reduces the number of unknown
macroscale and microscale variables by agglomerating close-by finite element
nodes into a limited number of clusters. Then, we use deflation methods to
project these variables into a lower-dimensional space where the material
elastoplastic behaviors are approximated. Finally, we solve for the unknown
variables and map them back to the original, high-dimensional space. We call
our method deflated clustering analysis and by comparing it to direct numerical
simulations we demonstrate that it accurately captures macroscale deformations
and microscopic effective responses. To illustrate the effect of microscale
pores on the macroscopic response of a cast component, we conduct multi-scale
simulations with spatially varying local heterogeneities that are modeled with
a microstructure characterization and reconstruction algorithm.
