Predicting plastic anisotropy using crystal plasticity and Bayesian neural network surrogate models

This work presents an efficient data-driven protocol to accurately predict plastic anisotropy from initial crystallographic texture. In this work, we integrated feed forward neural networks with Variational Bayesian Inference techniques to establish an accurate low-computational cost surrogate model...

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Bibliographic Details
Published inMaterials science & engineering. A, Structural materials : properties, microstructure and processing Vol. 833; p. 142472
Main Authors Montes de Oca Zapiain, David, Lim, Hojun, Park, Taejoon, Pourboghrat, Farhang
Format Journal Article
LanguageEnglish
Published Lausanne Elsevier B.V 26.01.2022
Elsevier BV
Subjects
Bayesian analysis
Bayesian neural network
Computer simulation
Crystal plasticity
Crystallography
Crystals
Neural networks
Plastic anisotropy
Plastic properties
Polycrystals
Simulation
Single crystals
Spherical harmonics
Statistical inference
Texture
Plastic anisotropy
Crystal plasticity
Bayesian neural network
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Summary:This work presents an efficient data-driven protocol to accurately predict plastic anisotropy from initial crystallographic texture. In this work, we integrated feed forward neural networks with Variational Bayesian Inference techniques to establish an accurate low-computational cost surrogate model capable of predicting the anisotropic constants based on the texture of the polycrystalline material with quantifiable uncertainty. The developed model was trained on the results of 54,480 crystal plasticity simulations. The performed simulations parametrized Hill’s anisotropic yield model for single crystals and polycrystalline textures, which were robustly represented using generalized spherical harmonics (GSH). Subsequently, the GSH-based representation of the different textures was linked to its corresponding Hill’s anisotropic coefficients using a variational Bayesian neural network. The efficacy and accuracy of the developed surrogate model were critically validated with the results of 20,000 new textures. The predictions from the Bayesian neural network model showed excellent agreement with results obtained from experiments and high-fidelity crystal plasticity finite element simulations.
ISSN:0921-5093
1873-4936
DOI:10.1016/j.msea.2021.142472