期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 116, 期 52, 页码 26414-26420出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1911815116
关键词
deep learning; data-driven modeling; recurrent neural network; plasticity
资金
- US National Science Foundation [CPS/CMMI-1646592]
- Air Force Office of Scientific Research [FA9550-18-1-0381]
- Department of Commerce National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design Grant [70NANB19H005]
- Department of Defense Vannevar Bush Faculty Fellowship [N00014-19-1-2642]
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.
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