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Meeting MS&T24: Materials Science & Technology
Symposium Integrated Computational Materials Engineering for Physics-Based Machine Learning Models
Presentation Title B-1: Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform
Author(s) Joshua Stickel, Brayan Murgas, Luke Brewer, Somnath Ghosh
On-Site Speaker (Planned) Joshua Stickel
Abstract Scope This presentation introduces a methodology for creating 2D image based, 3D statistically equivalent virtual microstructures (SEVMs) for polycrystalline materials with complex microstructures encompassing multi-modal morphological and crystallographic distributions. Cold spray formed (CSF) Al 7050 alloys, containing prior particles with coarse grains (CGs) and ultra-fine grains (UFGs) are one such example of these materials. An integrated SliceGAN-Dream3D platform is introduced consisting of a Generative Adversarial Network (GAN) to reconstruct complex morphology of multi-modal regions, with Dream3D for packing grains into these regions individually conforming to the experimental statistics in EBSD maps. A robust multiscale model is developed coupling crystal plasticity (CP) FEM for coarse-grained polycrystalline microstructures with an upscaled constitutive model (UCM) for modeling UFGs in a self-consistent manner. Finally, the micro-mechanical response is explored by averaging the response of sub-volume elements (SVEs). The SEVM generation and simulation constitute important components of image-based micromechanical modeling, necessary for microstructure-property relations.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Multiscale Simulation Investigation of Cavity Evolution in a Ni TPBAR Coating
Advanced Coupling of an FFT-Based Mesoscale Modeling Method to a Macroscale Finite Element Method
B-1: Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform
Deep Generative Model for Reproducing Microstructure of Low-Carbon Steel During Continuous Cooling
Deep Learning for Early Detection and Localization of Damage in Metal Plates
Developing Data-Driven Strength Models Incorporating Temperature and Strain-Rate Dependence
Hybrid Machine Learning Informed Design Guidelines for Structural Gradient Alloys with Enhanced Performances
Phase-Field Modeling of Grain Evolution and Recrystallization in Friction Stir Processing
PRISMS-MultiPhysics: An Open-Source Coupled Phase Field-Crystal Plasticity Framework and its Application to Simulate Twinning in Magnesium Alloys
Thermodynamic Integration for Dynamically Unstable Systems Using Interatomic Force Constants without Molecular Dynamics
Utilizing Convex Neural Networks to Predict the Yield Surfaces of Polycrystalline Samples with Complex Crystallographic Textures

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