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Title: Data driven modeling of plastic deformation

Abstract

In this paper the application of machine learning techniques for the development of constitutive material models is being investigated. A flow stress model, for strain rates ranging from 10–4 to 1012 (quasi-static to highly dynamic), and temperatures ranging from room temperature to over 1000 K, is obtained by beginning directly with experimental stress-strain data for Copper. An incrementally objective and fully implicit time integration scheme is employed to integrate the hypo-elastic constitutive model, which is then implemented into a finite element code for evaluation. Accuracy and performance of the flow stress models derived from symbolic regression are assessed by comparison to Taylor anvil impact data. The results obtained with the free-form constitutive material model are compared to well-established strength models such as the Preston-Tonks-Wallace (PTW) model and the Mechanical Threshold Stress (MTS) model. Here, preliminary results show candidate free-form models comparing well with data in regions of stress-strain space with sufficient experimental data, pointing to a potential means for both rapid prototyping in future model development, as well as the use of machine learning in capturing more data as a guide for more advanced model development.

Authors:
; ;
Publication Date:
Research Org.:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
OSTI Identifier:
1345168
Report Number(s):
LA-UR-16-27745
Journal ID: ISSN 0045-7825
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Accepted Manuscript
Journal Name:
Computer Methods in Applied Mechanics and Engineering
Additional Journal Information:
Journal Volume: 318; Journal Issue: C; Journal ID: ISSN 0045-7825
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 36 MATERIALS SCIENCE; strength models; high strain rate; J22 plasticity; machine learning; symbolic regression; Taylor anvil impact

Citation Formats

Versino, Daniele, Tonda, Alberto, and Bronkhorst, Curt A. Data driven modeling of plastic deformation. United States: N. p., 2017. Web. doi:10.1016/j.cma.2017.02.016.
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Versino, Daniele, Tonda, Alberto, & Bronkhorst, Curt A. Data driven modeling of plastic deformation. United States. https://doi.org/10.1016/j.cma.2017.02.016
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Versino, Daniele, Tonda, Alberto, and Bronkhorst, Curt A. Mon . "Data driven modeling of plastic deformation". United States. https://doi.org/10.1016/j.cma.2017.02.016. https://www.osti.gov/servlets/purl/1345168.
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@article{osti_1345168,
title = {Data driven modeling of plastic deformation},
author = {Versino, Daniele and Tonda, Alberto and Bronkhorst, Curt A.},
abstractNote = {In this paper the application of machine learning techniques for the development of constitutive material models is being investigated. A flow stress model, for strain rates ranging from 10–4 to 1012 (quasi-static to highly dynamic), and temperatures ranging from room temperature to over 1000 K, is obtained by beginning directly with experimental stress-strain data for Copper. An incrementally objective and fully implicit time integration scheme is employed to integrate the hypo-elastic constitutive model, which is then implemented into a finite element code for evaluation. Accuracy and performance of the flow stress models derived from symbolic regression are assessed by comparison to Taylor anvil impact data. The results obtained with the free-form constitutive material model are compared to well-established strength models such as the Preston-Tonks-Wallace (PTW) model and the Mechanical Threshold Stress (MTS) model. Here, preliminary results show candidate free-form models comparing well with data in regions of stress-strain space with sufficient experimental data, pointing to a potential means for both rapid prototyping in future model development, as well as the use of machine learning in capturing more data as a guide for more advanced model development.},
doi = {10.1016/j.cma.2017.02.016},
journal = {Computer Methods in Applied Mechanics and Engineering},
number = C,
volume = 318,
place = {United States},
year = {Mon May 01 00:00:00 EDT 2017},
month = {Mon May 01 00:00:00 EDT 2017}
}
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https://doi.org/10.1016/j.cma.2017.02.016
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    Works referencing / citing this record:

    Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering
    journal, August 2018

    • Dimiduk, Dennis M.; Holm, Elizabeth A.; Niezgoda, Stephen R.
    • Integrating Materials and Manufacturing Innovation, Vol. 7, Issue 3
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    Modeling Macroscopic Material Behavior With Machine Learning Algorithms Trained by Micromechanical Simulations
    journal, August 2019

    Application of artificial neural networks for the prediction of interface mechanics: a study on grain boundary constitutive behavior
    journal, January 2020

    • Fernández, Mauricio; Rezaei, Shahed; Rezaei Mianroodi, Jaber
    • Advanced Modeling and Simulation in Engineering Sciences, Vol. 7, Issue 1
    • DOI: 10.1186/s40323-019-0138-7

    A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites
    journal, October 2018

    • Lu, Xiaoxin; Giovanis, Dimitris G.; Yvonnet, Julien
    • Computational Mechanics, Vol. 64, Issue 2
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    Application of artificial neural networks for the prediction of interface mechanics: a study on grain boundary constitutive behavior
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    Data-driven computation for history-dependent materials
    journal, November 2019

    • Ladevèze, Pierre; Néron, David; Gerbaud, Paul-William
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    • DOI: 10.1016/j.crme.2019.11.008

    Shape-constrained Symbolic Regression -- Improving Extrapolation with Prior Knowledge
    text, January 2021

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