An inverse modeling approach has been used in this work to model the process of recrystallization using genetically evolving cellular automata (CA). After formulating a CA model for primary static recrystallization, differential evolution (DE), a real coded variant of genetic algorithms, has been used to search for the values of nucleation rate and a constant factor in growth rate. These parameters are difficult to determine experimentally. This provided an acceptable matching between the simulated and experimentally observed values of average grain size and aspect ratio in the microstructure. Here, a realistic initial microstructure of cold rolled copper with statistically similar parameters as the experimental ones has been generated using a simple inverse CA coupled to DE. Dislocation density has been seeded in the CA cells of the initial microstructure as per the experimental observations on the variation of hardness in the mesoscopic scale. Each CA cell was assigned a nucleation probability based on the dislocation density value seeded in it. Recovery phenomenon has been incorporated by decreasing the dislocation density with time. The decrease in dislocation density was incorporated based on the experimental data variation in hardness with annealing time, at temperatures below the recrystallization temperatures. A good match could be found between the simulated and experimental parameters of the microstructure. Two simulated microstructures are shown in Figures 1 and 2.

Fig. 1 At time step =1, Nucleation starts.

Fig. 2. At time step =6, Recrystallization partly over

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