Microstructural simulation of dynamic recrystallization

A Monte Carlo model for dynamic recrystallization has been developed from earlier models used to simulate static recrystallization and grain growth. The model simulates dynamic recrystallization by adding recrystallization nuclei and stored energy continuously with time. The simulations reproduce many of the essential features of dynamic recrystallization. The stored energy of the system, which may be interpreted as a measure of the flow stress, goes through a maximum and then decays, monotonically under some conditions and in an oscillatory manner under others. The principle parameters that were studied were the rate of adding stored energy, [Delta]H, and the rate of adding nuclei, [Delta]N. As [Delta]H increases, for fixed [Delta]N, the oscillations decay more rapidly and the asymptotic energy rises. As [Delta]N increases again the oscillations decay more rapidly but the asymptotic stored energy decreases. The mean grain size of' the system also oscillates in a similar manner to the stored energy but out of phase by 90[deg]. The flow stress oscillations occurred for conditions which lead to both coarsening and refinement of the initial grain size. Necklacing of the prior grain structure by new grains were observed for low [Delta]H and high [Delta]N; it is, however, not an invariable feature of grain refinement. The initial grain size has a profound influence on the microstructure that evolves during the first cycle of recrystallization but at long times, a mean grain size is established which depends on the values of [Delta]H and [Delta]N alone. Comparison of the relationships between the energy storage rate, maximum and asymptotic stored energy and the grain size suggest that in physical systems the energy storage rate and the nucleation rate are coupled. Comparison of the simulation results with experimental trends suggests that the dependence of nucleation rate on storage should be positive but weak. All of these results were obtained without the addition of special parameters to the model.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/30270/1/0000671.pd