Understanding and predicting how microstructure evolves has become tremendously important to enable optimization of processing for improved properties. Modeling microstructural evolution during recrystallization and grain growth of polycrystalline material is gaining importance in the metals processing industry. Alloys can first be designed computationally and then their mechanical properties may be optimized by controlling their microstructural features. These features consist of grain size, particle/precipitate content, recrystallization fraction, and crystallographic texture. Moreover, given the complexity of industrial thermo-mechanical processes and the lack of comprehensive understanding of various annealing phenomena such as recrystallization, numerous efforts have concentrated on the improvement of computer simulation for recrystallization and grain growth.;The Monte Carlo Potts model was used to predict accurately the microstructural evolution during recrystallization taking into account the effect of stored energy around coarse particles. The spatial distribution of these coarse particles was specified as input for the simulation approach based on a statistical descriptor (pair correlation Function) for 2D section planes of AA7050 structure.;Finally, to prove the benefits of integrating the experiment into the simulation model and make the simulation more realistic an initial structure was obtained a real as-deformed microstructure by Electron Back scatter diffraction (EBSD) as well as the second phase particles distribution was determined by Backscattered Electrons (BSE).
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