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The main fundamental problem in studying plasticity and microstructure evolution is that they depend on five components of the plastic strain tensor εp , its entire path εp , and pressure p and its path p , which leaves little hope of finding some general laws, especially at severe plastic straining and high pressures. Here, we review the validity of the following hypothesis for quasi-static material behavior after some critical level of cold severe plastic strain and some straining paths: initially isotropic polycrystalline materials behave like perfectly plastic, isotropic, and strain-path-independent with the corresponding limit surface of perfect plasticity and reach steady values of the crystallite/grain size and dislocation density, which are strain- and strain-path-independent. However, there are multiple steady microstructural states and corresponding limit surfaces of perfect plasticity. The main challenge is to find for which classes of loading paths ε path p path and p material behaves along the same limit surface of perfect plasticity and steady microstructural state and for which loading paths p path ε path p and there is a jump to the different limit surface of perfect plasticity and steady microstructural state. Various experimental, computational, and coupled experimental-computational techniques are analyzed, and some controversies and challenges are summarized.
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Deep learning predicts path-dependent plasticity
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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- Award ID(s):
- 1646592
- PAR ID:
- 10129616
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 116
- Issue:
- 52
- ISSN:
- 0027-8424
- Page Range / eLocation ID:
- 26414 to 26420
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1073/pnas.1911815116
