Deep learning predicts path-dependent plasticity
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 16, 2019
- Source ID
- 10.1073/pnas.1911815116
Entities
People
- Jian Cao
- Kornel F. Ehmann
- M. Mozaffar
- Miguel A Bessa
- R. Bostanabad
- Wei Chen
Organizations
- Air Force Office of Scientific Research
- Delft University of Technology
- National Institute of Standards and Technology
- Northwestern University
- United States Department of Commerce
- University of California