A cellular automata model for recrystallization annealing of aged GH4169 superalloy and its application

Abstract

For the forging of GH4169, there are often serious mixed and coarse grains that need to be uniformly refined through a multi-stage annealing process. However, it will need to take a lot of time and cost to explore suitable multi-stage annealing process parameters if we only employ the experimental method. The cellular automata (CA) simulation can reveal the evolution of microstructure during forming and annealing, which can obviously reduce the number of experiments. Therefore, in the study, a CA model to simulate the evolution of grain microstructure, dislocation density and δ phase is established. The results demonstrate that the established CA model can effectively predict the microstructural evolution of GH4169. The correlation coefficient exceeds 0.94. Furthermore, using the CA simulation, an optimized multi-stage annealing parameter of “1000 ℃×3min+1000 ℃-5 min-950 ℃+950 ℃×50min” has been identified, which is also verified by experiments. These findings further verify the accuracy and reliability of the model in simulating microstructural evolution during the annealing process of GH4169 forgings. Eventually, based on the CA simulation, the formation mechanism for the uneven grain growth in the annealed microstructure is revealed. At original deformed grain boundaries, dynamic recrystallization (DRX) grains grow slowly, whereas static recrystallization (SRX) grains at DRX boundaries or δ phase boundaries within original deformed grains grow rapidly.