Distance Minimizing-Based Data-Driven Computational Plasticity Method with Fixed Dataset

Abstract

A data-driven computational plasticity method based on the distance minimizing framework is proposed in this paper. In this method, the internal variables in conventional plasticity are abandoned and a fixed dataset considering path-dependent behaviors of materials is constructed. With the fixed dataset, a stress correspondence method is developed to compute the plastic strain of every integration point at each load step, and a data-driven classification model for yielding is constructed to rapidly determine the yield status of each point in the method. Moreover, a symmetric mapping method is developed to accurately determine the stress–strain state of the integration point under unloading or inverse loading conditions. Several representative examples are presented to show the capability of the proposed method. Numerical results of two- and three-dimensional truss structures and three-dimensional continuum bodies demonstrate the high efficiency and accuracy of the proposed data-driven computational plasticity method.