Deep learning predicts path-dependent plasticity

Mozaffar, Mojtaba; Bostanabad, Ramin; Chen, W.; Ehmann, Kornel F.; Cao, Jian; Bessa, Miguel A.

doi:10.1073/pnas.1911815116

articleProceedings of the National Academy of SciencesDec 16, 2019HYBRID OA

Deep learning predicts path-dependent plasticity

MMMojtaba Mozaffar RBRamin Bostanabad WCW. Chen KFKornel F. Ehmann JCJian Cao

Northwestern University · University of California, Irvine · +2 more institutions

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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…

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Topics & keywords

Topics

Keywords

Yield surface
Plasticity
Stress path
Context (archaeology)
Equivalence (formal languages)
Computer science
Work hardening
Statistical physics

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