About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
Data-Driven Reduced-Order Multiscale Materials Modeling Under Inhomogeneous Porosity Distributions |
Author(s) |
Shiguang Deng, Carl Soderhjelm, Diran Apelian, Ramin Bostanabad |
On-Site Speaker (Planned) |
Shiguang Deng |
Abstract Scope |
Cast aluminum alloys often contain non-uniformly distributed pores of complex morphologies. Since such porosity defects have significant influence on material behaviors and affect the usage in high-performance applications, it is vital to understand the cross-scale impact of microscale porosity characteristics on the cast component’s macro-mechanical properties. In this talk, we will introduce a computationally efficient data-driven multiscale framework to simulate the behavior of metallic components containing process-induced porosity distributions. Major components of our approach include: (1) a porosity-oriented 3D microstructure reconstruction algorithm which mimics the material’s local heterogeneity with reconstructed pores from tomography characterization, (2) a novel reduced-order model which significantly reduces computational costs by projecting solution variables into a lower dimensional space where the material’s elasto-plastic behaviors are approximated, and (3) a machine learning-based metamodel which correlates material responses with pore morphology and deformation history. We will compare our approach against direct numerical simulations to demonstrate performance and versatility. |
Proceedings Inclusion? |
Undecided |