Deep learning predicts path-dependent plasticity

M. Mozaffar, R. Bostanabad, W. Chen, K. Ehmann, Jian Cao*, M. A. Bessa

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

71 Scopus citations

Abstract

Plasticity theory aims at describing the yield loci and work hardening of a material under general deformation states. Most of its complexity arises from the nontrivial dependence of the yield loci on the complete strain history of a material and its microstructure. This motivated 3 ingenious simplifications that underpinned a century of developments in this field: 1) yield criteria describing yield loci location; 2) associative or nonassociative flow rules defining the direction of plastic flow; and 3) effective stress-strain laws consistent with the plastic work equivalence principle. However, 2 key complications arise from these simplifications. First, finding equations that describe these 3 assumptions for materials with complex microstructures is not trivial. Second, yield surface evolution needs to be traced iteratively, i.e., through a return mapping algorithm. Here, we show that these assumptions are not needed in the context of sequence learning when using recurrent neural networks, diverting the above-mentioned complications. This work offers an alternative to currently established plasticity formulations by providing the foundations for finding history- and microstructure-dependent constitutive models through deep learning.

Original languageEnglish (US)
Pages (from-to)26414-26420
Number of pages7
JournalProceedings of the National Academy of Sciences of the United States of America
Volume116
Issue number52
DOIs
StatePublished - Dec 26 2019

Keywords

  • Data-driven modeling
  • Deep learning
  • Plasticity
  • Recurrent neural network

ASJC Scopus subject areas

  • General

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