Study of Recrystallization in Metals through Equiaxial Growth of Nuclei by Computational Simulation with Three-Dimensional Hybrid Cellular Automata

Cellular Automata (CA) are powerful simulation tools that operate through discrete elements and associations. CA undirected neighborhood searches may produce specific grain shapes (e.g., octahedral, cuboctahedral, cubic), but not spherical ones. However, in recrystallization modeling, the spherical shape plays a crucial role. Thus, Hybrid Cellular Automata (HCA) share several properties with CA but operate in both continuous and discrete modes simultaneously. Unlike deterministic CA, HCA enable equiaxial grain growth without the need for additional correction algorithms, generating grains that remain initially spherical until they encounter boundaries. This behavior has been addressed by other simulation techniques, but HCA provide a simple and highly effective alternative. In this work, HCA are described in detail, including their foundational principles, algorithm performance, calibration, and potential results. The findings highlight the capability of HCA for straightforward and accurate recrystallization simulations with equiaxial growth.