State-of-the-Art Review of the Simulation of Dynamic Recrystallization

Abstract

The evolution of microstructures during the hot working of metallic materials determines their workability and properties. Recrystallization is an important softening mechanism in material forming that has been extensively researched in recent decades. This paper comprehensively reviews the basic methods and their applications in numerical simulations of dynamic recrystallization (DRX). The advantages and shortcomings of simulation methods are evaluated. Mean field models are used to implicitly describe the DRX process and are embedded into a finite element (FE) program for forming. These models provide recrystallization volume fraction and average grain size in the FE results without requiring extra computational resources. However, they do not accurately describe the microphysical mechanism, leading to a lower simulation accuracy. On the other hand, full field methods explicitly predict grain topology on a mesoscopic scale, fully considering the microscopic physical mechanism. This enhances the simulation accuracy but requires a significant amount of computational resources. Recently, the coupling of full field methods with polycrystal plasticity models and precipitation models has rapidly developed, considering more influencing factors of recrystallization on a microscale. Furthermore, integration with evolving machine learning methods has the potential to significantly improve the accuracy and efficiency of recrystallization simulation.