Computationally Efficient Cellular Automata-Based Full-Field Models of Static Recrystallization: A Perspective Review

The evolution of metallic material microstructures under thermomechanical treatment is a critical element that influences the in-use properties of the final product. In the last four decades, many researchers around the world focused on reliable numerical recreation of phenomena that occur during metal processing. Particular attention is put on the phase transformation, texture evolution, and recrystallization predictions to simulate material behavior at the subsequent processing stages. In recent years, approaches taking into account explicit representation of morphological elements during simulation are becoming more frequently used even at the industrial scale. One of those methods addressed within the current article in application to static recrystallization (SRX) modeling is the cellular automata (CA) approach. A review of the CA-based full-field SRX models highlights the progress in capabilities of developed approaches in consideration of various mechanisms controlling microstructure evolution like heterogeneous energy distribution, probabilistic or physics-based nucleation, grain growth driven by different driving forces, or grain boundary migration is presented within the article. The issue of the computational time associated with 3D CA modeling is particularly addressed. Possible approaches to reducing simulation times based on efficient use of modern computer architecture are finally evaluated as guidelines for further model development.

Automated summary

The evolution of metallic material microstructures under thermomechanical treatment has a significant impact on the in-use properties of the final product. In recent years, methods that explicitly represent morphological elements have been increasingly used in static recrystallization (SRX) modeling. This article reviews the progress and capabilities of cellular automata (CA)-based full-field SRX models, highlighting various mechanisms controlling microstructure evolution, such as energy distribution, nucleation, grain growth, and grain boundary migration. The article also addresses the computational time issue associated with 3D CA modeling and evaluates possible approaches for reducing simulation times.