Machine Learning–Based Reduce Order Crystal Plasticity Modeling for ICME Applications
Abstract
Abstract Crystal plasticity simulation is a widely used technique for studying the deformation processing of polycrystalline materials. However, inclusion of crystal plasticity simulation into design paradigms such as integrated computational materials engineering (ICME) is hindered by the computational cost of large-scale simulations. In this work, we present a machine learning (ML) framework using the material information platform, Open Citrination, to develop and calibrate a reduced order crystal plasticity model for face-centered cubic (FCC) polycrystalline materials, which can be both rapidly exercised and easily inverted. The reduced order model takes crystallographic texture, constitutive model parameters, and loading condition as inputs and returns the stress-strain curve and final texture. The model can also be inverted and take a stress-strain curve, loading condition, and final texture as inputs and return the initial texture and constitutive model parameters as outputs. Principal component analysis (PCA) is used to develop an efficient description of the crystallographic texture. A viscoplastic self-consistent (VPSC) crystal plasticity solver is used to create the training data by modeling the stress-strain behavior and evolution of texture during deformation processing.
- Authors:
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1780461
- Grant/Contract Number:
- FE0027776
- Resource Type:
- Published Article
- Journal Name:
- Integrating Materials and Manufacturing Innovation
- Additional Journal Information:
- Journal Name: Integrating Materials and Manufacturing Innovation Journal Volume: 7 Journal Issue: 4; Journal ID: ISSN 2193-9764
- Publisher:
- Springer Science + Business Media
- Country of Publication:
- Germany
- Language:
- English
Citation Formats
Yuan, Mengfei, Paradiso, Sean, Meredig, Bryce, and Niezgoda, Stephen R. Machine Learning–Based Reduce Order Crystal Plasticity Modeling for ICME Applications. Germany: N. p., 2018.
Web. doi:10.1007/s40192-018-0123-x.
Yuan, Mengfei, Paradiso, Sean, Meredig, Bryce, & Niezgoda, Stephen R. Machine Learning–Based Reduce Order Crystal Plasticity Modeling for ICME Applications. Germany. https://doi.org/10.1007/s40192-018-0123-x
Yuan, Mengfei, Paradiso, Sean, Meredig, Bryce, and Niezgoda, Stephen R. Tue .
"Machine Learning–Based Reduce Order Crystal Plasticity Modeling for ICME Applications". Germany. https://doi.org/10.1007/s40192-018-0123-x.
@article{osti_1780461,
title = {Machine Learning–Based Reduce Order Crystal Plasticity Modeling for ICME Applications},
author = {Yuan, Mengfei and Paradiso, Sean and Meredig, Bryce and Niezgoda, Stephen R.},
abstractNote = {Abstract Crystal plasticity simulation is a widely used technique for studying the deformation processing of polycrystalline materials. However, inclusion of crystal plasticity simulation into design paradigms such as integrated computational materials engineering (ICME) is hindered by the computational cost of large-scale simulations. In this work, we present a machine learning (ML) framework using the material information platform, Open Citrination, to develop and calibrate a reduced order crystal plasticity model for face-centered cubic (FCC) polycrystalline materials, which can be both rapidly exercised and easily inverted. The reduced order model takes crystallographic texture, constitutive model parameters, and loading condition as inputs and returns the stress-strain curve and final texture. The model can also be inverted and take a stress-strain curve, loading condition, and final texture as inputs and return the initial texture and constitutive model parameters as outputs. Principal component analysis (PCA) is used to develop an efficient description of the crystallographic texture. A viscoplastic self-consistent (VPSC) crystal plasticity solver is used to create the training data by modeling the stress-strain behavior and evolution of texture during deformation processing.},
doi = {10.1007/s40192-018-0123-x},
journal = {Integrating Materials and Manufacturing Innovation},
number = 4,
volume = 7,
place = {Germany},
year = {Tue Dec 18 00:00:00 EST 2018},
month = {Tue Dec 18 00:00:00 EST 2018}
}
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