Orthotropic behavior characterization in sheet metal forming: parameter identification of Yld2011-27p model using deep learning and genetic algorithm

Yld2011-27p is a highly accurate criterion for modeling anisotropic material behavior, though its anisotropic parameter identification traditionally requires extensive experimentation. The present study proposes two data-driven approaches, direct and indirect, based on the earing geometry of a single deep drawing test for efficiently determining these parameters. A deep neural network (DNN) trained by preliminary finite element data is used in the direct method while a combination of deep neural network and genetic algorithm (GA) is used in the indirect method to calibrate the Yld2011-27p anisotropic parameters. These models are iteratively updated through finite element simulations via a Python script and an Abaqus VUMAT subroutine, until the simulated results align with experimental observations. The entire process is automated, requiring only the experimental output and parameter bounds from the user. The approach significantly reduces experimental effort while achieving high prediction accuracy. The direct and indirect frameworks reached final contour prediction errors of 0.94 mm and 0.88 mm, respectively, which are lower than the error of the experimentally calibrated parameters (0.97 mm).