Efficient and Robust Crystal Plasticity Parameter Identification via RSM-GA Coupling: Application to AZ31 Magnesium...

Identifying material parameters in crystal plasticity constitutive models with high precision and high efficiency can be especially complicated due to the ever-increasing complexity of the models. These material parameters are typically calibrated through the fitting of macroscale experimental data, such as true stress-strain curves, while microscale experimental data, including phase evolution, twinning volume fraction, and so on, are rarely considered and used for verification. In the present study, a novel and computationally efficient optimization procedure for material parameters identification in a crystal plasticity constitutive model has been proposed, which couples a response surface model (RSM) and a genetic algorithm (GA). Specifically, 34 macroscopic true stress-strain data (21 for rolling direction, RD, and 13 for transverse direction, TD) and 4 microscale {10-12} extension twin (ET) volume fraction data have been utilized for multi-objective training. Furthermore, the objective function has been optimized in the present study by tailoring the weights of macroscale stress-strain data and microscale volume fractions for {10-12} ET. The proposed optimization methodology has been verified via visco-plastic self-consistent (VPSC) simulation of tensile deformation for AZ31 magnesium (Mg) alloy sheet with bimodal non-basal texture at room temperature. Results show that the fitness value of the optimization procedure would rapidly converge to a stable value of ~80 within 200 iterations. The obtained material parameters for VPSC simulation on the basis of RD-tensile and TD-tensile experimental data show good validity and applicability in aspects of mechanical response, activities of involved deformation mechanisms, evolution of volume fraction for {10-12} ET, and characteristics of texture evolution.