Simulation of microstructural changes after hot deformation of aluminum–magnesium alloy using cellular automata

Abstract

In this work, the kinetics of recovery and recrystallization as well as microstructural changes after hot deformation of 5083 aluminum alloy were predicted using cellular automata. A neural network modeling was first developed to assess flow stress behavior of the alloy during hot deformation. Afterwards, the kinetics of static recovery was evaluated by means of dislocation theory and a first-order differential equation while the results of the neural network model were taken as the initial condition. Moreover, at high temperature i.e., 300 °C or higher, the occurrence of static recrystallization was also simulated employing two-dimensional cellular automata. To assess physical parameters, validation of the model and construction of the neural network, single-hit and double-hit tensile tests together with microstructural evolutions were conducted at temperatures ranging between 180 and 380 °C. Comparing the real and simulated microstructures showed a good consistency indicating that the modeling was working properly. The model is capable of considering the impact of different parameters such as the initial grain size, the pre-strain and holding time on the rate of softening and the final microstructures. The results showed that static recovery was the main softening process at temperatures 180–260 °C, however, the rate of static recovery was slightly reduced at 260 °C owing to the occurrence of dynamic recovery during hot deformation. On the other hand, static recrystallization became the dominant softening mechanism at 300 °C or higher temperatures and the activation energies for nucleation and growth processes during recrystallization were computed as 141 kJ/mole and 153 kJ/mole.