Coupled crystal plasticity – Probabilistic cellular automata approach to model dynamic recrystallization in magnesium alloys

This study presents a framework to simulate dynamic recrystallization (DRX) in hexagonal closed packed (HCP) metals and alloys using crystal plasticity based finite element model (CPFEM) coupled with a probabilistic cellular automata (CA) approach, as applied to Mg alloys. The CPFEM takes as input the microstructural information from experimental measurements and computes local dislocation density evolution corresponding to active deformation modes. DRX proceeds via nucleation of new grains and their subsequent growth. A new nucleation criterion based on local mismatch in dislocation density is implemented in the model. Nucleation sites are defined solely from the local inhomogeneouty of dislocation density within a grain or across grain boundaries. Cellular automata model with probabilistic state switching rule predicts the growth of viable nucleation sites with high misorientation angle depending on the difference in the stored energy of the nucleus and the stored energy of the surrounding matrix. State switching probability rule is based on the velocity of the grain boundary between the nucleus and the matrix grains. The new approach is validated with recrystallization data on AZ31 sheets. The model captures both the microscopic (texture) and the macroscopic (stress–strain response) properties during DRX.