Data driven modeling of plastic deformation

Versino, Daniele; Tonda, Alberto; Bronkhorst, Curt A.

doi:10.1016/j.cma.2017.02.016

Title: Data driven modeling of plastic deformation

Journal Article · Mon May 01 00:00:00 EDT 2017 · Computer Methods in Applied Mechanics and Engineering

DOI:https://doi.org/10.1016/j.cma.2017.02.016· OSTI ID:1345168

Versino, Daniele; Tonda, Alberto; Bronkhorst, Curt A.

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.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1345168

Report Number(s):: LA-UR-16-27745

Journal Information:: Computer Methods in Applied Mechanics and Engineering, Vol. 318, Issue C; ISSN 0045-7825

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 39 works

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Modeling Macroscopic Material Behavior With Machine Learning Algorithms Trained by Micromechanical Simulations Reimann, Denise; Nidadavolu, Kapil; ul Hassan, Hamad Frontiers in Materials, Vol. 6 https://doi.org/10.3389/fmats.2019.00181	journal	August 2019
Application of artificial neural networks for the prediction of interface mechanics: a study on grain boundary constitutive behavior Fernández, Mauricio; Rezaei, Shahed; Rezaei Mianroodi, Jaber Advanced Modeling and Simulation in Engineering Sciences, Vol. 7, Issue 1 https://doi.org/10.1186/s40323-019-0138-7	journal	January 2020
A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites Lu, Xiaoxin; Giovanis, Dimitris G.; Yvonnet, Julien Computational Mechanics, Vol. 64, Issue 2 https://doi.org/10.1007/s00466-018-1643-0	journal	October 2018
Prediction of stress-strain curves for aluminium alloys using symbolic regression Kabliman, Evgeniya; Kolody, Ana Helena; Kommenda, Michael PROCEEDINGS OF THE 22ND INTERNATIONAL ESAFORM CONFERENCE ON MATERIAL FORMING: ESAFORM 2019, AIP Conference Proceedings https://doi.org/10.1063/1.5112747	conference	January 2019
Application of artificial neural networks for the prediction of interface mechanics: a study on grain boundary constitutive behavior Fernández, Mauricio; Rezaei, Shahed; Rezaei Mianroodi, Jaber RWTH Aachen University https://doi.org/10.18154/rwth-2021-02240	text	January 2020
Data-driven computation for history-dependent materials Ladevèze, Pierre; Néron, David; Gerbaud, Paul-William Comptes Rendus Mécanique, Vol. 347, Issue 11 https://doi.org/10.1016/j.crme.2019.11.008	journal	November 2019
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