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Title: Predicting the mechanical response of oligocrystals with deep learning

Abstract

In this study, we employ data-driven homogenization approaches to predict the particular mechanical evolution of polycrystalline aggregates with tens of individual crystals. In these oligocrystals the differences in stress response due to microstructural variation is pronounced. Shell-like structures produced by metal-based additive manufacturing and the like make the prediction of the behavior of oligocrystals technologically relevant. The predictions of traditional homogenization theories based on grain volumes are not sensitive to variations in local grain neighborhoods. Direct simulation of the local response with crystal plasticity finite element methods is more detailed, but the computations are expensive. To represent the stress-strain response of a polycrystalline sample given its initial grain texture and morphology we have designed a novel neural network that incorporates a convolution component to observe and reduce the information in the crystal texture field and a recursive component to represent the causal nature of the history information. This model exhibits accuracy on par with crystal plasticity simulations at minimal computational cost per prediction.

Authors:
 [1];  [1];  [1];  [1]
  1. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1570256
Alternate Identifier(s):
OSTI ID: 1530642
Report Number(s):
SAND2019-10358J
Journal ID: ISSN 0927-0256; 679027
Grant/Contract Number:  
AC04-94AL85000
Resource Type:
Accepted Manuscript
Journal Name:
Computational Materials Science
Additional Journal Information:
Journal Volume: 169; Journal Issue: C; Journal ID: ISSN 0927-0256
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; crystal plasticity; additive manufacturing; convolutional neural networks; recurrent neural networks; microstructure

Citation Formats

Frankel, A. L., Jones, R. E., Alleman, C., and Templeton, J. A. Predicting the mechanical response of oligocrystals with deep learning. United States: N. p., 2019. Web. doi:10.1016/j.commatsci.2019.109099.
Frankel, A. L., Jones, R. E., Alleman, C., & Templeton, J. A. Predicting the mechanical response of oligocrystals with deep learning. United States. doi:10.1016/j.commatsci.2019.109099.
Frankel, A. L., Jones, R. E., Alleman, C., and Templeton, J. A. Fri . "Predicting the mechanical response of oligocrystals with deep learning". United States. doi:10.1016/j.commatsci.2019.109099.
@article{osti_1570256,
title = {Predicting the mechanical response of oligocrystals with deep learning},
author = {Frankel, A. L. and Jones, R. E. and Alleman, C. and Templeton, J. A.},
abstractNote = {In this study, we employ data-driven homogenization approaches to predict the particular mechanical evolution of polycrystalline aggregates with tens of individual crystals. In these oligocrystals the differences in stress response due to microstructural variation is pronounced. Shell-like structures produced by metal-based additive manufacturing and the like make the prediction of the behavior of oligocrystals technologically relevant. The predictions of traditional homogenization theories based on grain volumes are not sensitive to variations in local grain neighborhoods. Direct simulation of the local response with crystal plasticity finite element methods is more detailed, but the computations are expensive. To represent the stress-strain response of a polycrystalline sample given its initial grain texture and morphology we have designed a novel neural network that incorporates a convolution component to observe and reduce the information in the crystal texture field and a recursive component to represent the causal nature of the history information. This model exhibits accuracy on par with crystal plasticity simulations at minimal computational cost per prediction.},
doi = {10.1016/j.commatsci.2019.109099},
journal = {Computational Materials Science},
number = C,
volume = 169,
place = {United States},
year = {2019},
month = {11}
}

Journal Article:
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This content will become publicly available on November 1, 2020
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