TY - JOUR
T1 - Deep learning predicts path-dependent plasticity
AU - Mozaffar, M.
AU - Bostanabad, R.
AU - Chen, W.
AU - Ehmann, K.
AU - Cao, Jian
AU - Bessa, M. A.
N1 - Funding Information:
ACKNOWLEDGMENTS. We acknowledge support by US National Science Foundation Grant CPS/CMMI-1646592, Air Force Office of Scientific Research Grant FA9550-18-1-0381, Department of Commerce National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design Grant 70NANB19H005, and Department of Defense Vannevar Bush Faculty Fellowship N00014-19-1-2642.
Publisher Copyright:
© 2019 National Academy of Sciences. All rights reserved.
PY - 2019/12/26
Y1 - 2019/12/26
N2 - 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.
AB - 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.
KW - Data-driven modeling
KW - Deep learning
KW - Plasticity
KW - Recurrent neural network
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U2 - 10.1073/pnas.1911815116
DO - 10.1073/pnas.1911815116
M3 - Article
C2 - 31843918
AN - SCOPUS:85077301759
VL - 116
SP - 26414
EP - 26420
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
SN - 0027-8424
IS - 52
ER -