Static Recrystallization Modeling With a Cellular Automata Algorithm

This paper reports on one part of a research project supported by NSF, which aims at developing a multi-scale methodology for systematic microstructure prediction in thermo-mechanical processing of metals. Based on combining mesoscopic microstructure models with macroscopic process formulations, the methodology is expected to provide universally applicable and accurate microstructure prediction capabilities. Cellular Automata (CA) models have been widely used in scientific studies of various microstructural phenomena. This paper discusses the modeling of the static recrystallization phenomenon by employing a regular CA algorithm. The recrystallization processes of single-phase systems under different nucleation conditions are simulated followed by the recrystallization kinetics analysis for 200 × 200 two-dimensional lattice. The performed simulations of static recrystallization confirm that the recrystallized volume fractions are time dependent. Furthermore, the simulated microstructures validate the following Johnson-Mehl-Avrami-Kolmogorov (JMAK) model according to which the recrystallized volume fraction is a sigmoidal function of time, and their evolution matches the JMAK equation with the expected exponents.