Machine learning-driven stress integration method for anisotropic plasticity in sheet metal forming

Abstract

This study proposes a machine learning-based constitutive model for anisotropic plasticity in sheet metals. A fully connected deep neural network (DNN) is constructed to learn the stress integration procedure under the plane stress condition. The DNN utilizes the labeled training data for feature learning, and the respective dataset is generated numerically based on the Euler-backward method for the whole loading domains with one element simulation. The DNN is trained sufficiently to learn all the incremental loading paths of the input-output stress pair by using advanced anisotropic yield functions. Its performance with anisotropy is evaluated for the predictions of r-values and normalized yield stress ratios along 0–90 ° to the rolling direction. In addition, the trained DNN is then incorporated in user material subroutine UMAT in ABAQUS/Implicit. Thereafter, the DNN-based anisotropic constitutive model is tested with a cup drawing simulation to evaluate earing profile. The obtained earing profile is compatible with the one from the trained anisotropic yield function.