Reduced-order multiscale modeling of plastic deformations in 3D alloys with spatially varying porosity by deflated clustering analysis

Abstract

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’s 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.

A Data-Driven Framework for Designing Microstructure of Multifunctional Composites with Deep-Learned Diffusion-Based Generative Models

“…Moreover, the FEM analysis is unsuitable for design and optimization problems which necessitate computationally expensive iterative analysis. As a result, reduced order models (ROMs) are introduced to decrease computational costs while maintaining high accuracy and versatility [28]. Some notable ROMs are based on the principle component analysis (PCA) [29], proper generalized decomposition (PGD) [30], fast Fourier transformation (FFT) [31], transformation filed analysis (TFA) [32], nonuniform transformation field analysis (NTFA) [33], proper orthogonal decomposition (POD) [34], and self-consistent clustering analysis (SCA) [35].…”

Second‐order defeaturing estimator of manufacturing‐induced porosity on structural elasticity

“…This model could represent pore spatial ramifications by adjusting aspect ratios of ellipsoids. Deng et al developed a concurrent multiscale model in Reference 14 to predict the impacts of spatially varying porosity on engineered alloys. Their model integrates with a microstructure reconstruction algorithm to allow for complex microscale morphologies with various pore descriptors including pore volume fraction, number, size, geometry, and neighbor distances.…”