Towards optimizing the thermal processes in aluminum alloys using a full-field CA based approach for static recrystallization modeling

A combination of thermal and mechanical processing is used to produce flat rolled aluminum products. Typically, hot rolled sheets undergo significant time at elevated temperatures during coil cooling. This results in static recrystallization. It is important to understand the linkage between the annealing schedule and the microstructure development to design robust manufacturing process that maximizes product performance and minimizes material loss in the subsequent product manufacturing. To achieve this, accurate process-microstructure linkage models are needed. This work proposes a framework capable of handling complex annealing schedules and can be used to predict microstructure evolution and the kinetics of recrystallization. The framework uses measured data like the electron backscatter diffraction maps and the annealing schedule as inputs. It uses the measured data to calculate internal variables like the stored energy and predict the evolved microstructure. The results are validated with measured data. The proposed model can further be utilized to optimize the manufacturing process while minimizing expensive plant trials.