Modeling and simulation of recrystallization in metals with spheroidal grain structures using hybrid cellular automata

ABSTRACT Hybrid Cellular Automata (HCA) are robust computational tools that extend the classical Cellular Automata (CA) framework by integrating both discrete and continuous modeling principles. This hybrid nature enables more accurate, flexible, and efficient simulations of complex microstructural phenomena. In this work, a three-dimensional HCA approach is employed to simulate the recrystallization of metallic materials with anisotropic spheroidal grains, specifically, prolate and oblate morphologies. The proposed model introduces geometric and mathematical formulations that enable control over grain shape and spatial orientation during growth, thus extending previous models limited to spherical symmetry. The implementation is entirely computational, developed in Python, and structured for modularity and efficiency. Several simulations were conducted under varying conditions, including single and multiple nuclei, different grid sizes, and controlled anisotropic orientations. These simulations demonstrate the model’s capacity to generate realistic and heterogeneous microstructures, with adjustable morphological and directional features. Quantitative analyses of grain volumes and growth patterns further validate the model’s precision and consistency. Additionally, the work explores spatial rotation techniques to simulate heterogeneous anisotropic textures within a single matrix, enabling the modeling of complex structures. The flexibility, ease of implementation, and qualitative accuracy of this HCA model establish it as a powerful tool for predictive analysis in computational materials science.