A Comparative Study of the effect of Random and Preferred Crystallographic Orientations on Dynamic Recrystallization Behavior using a Cellular Automata Model

Abstract Dynamic recrystallization (DRX) in a Ni alloy has been studied using a cellular automata model. The dislocation density evolves according to the Kocks-Mecking equation and nucleation is modeled according to an Arrhenius type equation. Nucleation occurs at grain boundaries when their dislocation densities in neighboring grans exceed a threshold value. A new methodology is developed to predict the nucleation probability for the selection of recrystallizing grains based on the deformation temperature and strain rate. Grain growth is modeled based on a driving pressure and grain boundary mobility, which are functions of the grain boundary energy and misorientation between neighboring grains, respectively. The flow stress response and grain size evolution of the material have been found to closely agree with experimental and simulated results from literature. We have systematically analyzed the effect of various combinations of preferred and random orientations of both pre-existing and new grains on the microstructure evolution as a result of DRX and growth. The growth kinetics have been determined for all orientation combinations. It is identified that the DRX kinetics are fastest when preferred crystallographic orientations are assigned to both pre-existing and new grains. Through inverse pole figures and pole figures, the weakening of texture intensity has been observed in cases when random orientation is assigned to new grains and vice versa for the reverse case. The shifting of the direction of peak crystallographic texture intensity has been observed in several cases when random orientation is assigned to new grains, hereby implying grain re-orientation. Additionally, the effect of randomness has been evaluated by conducting three simulations of each set of orientations. The present study can help design thermo-mechanical treatments and metal forming process schedules to achieve tailored sets of mechanical properties for specific applications.