9th International Conference on Modeling and Simulation of Metallurgical Processes in Steelmaking - STEELSIM2021

Identification of the three-dimensional cellular automata (CA) model of static recrystallization (SRX) based on the inverse analysis concept is the primary goal of the research. The developed CA model is a full-field approach that captures local heterogeneities in grain morphology, crystallographic orientation, and distribution of stored energy after deformation. The identification stage is based on the inverse analysis concept that combines a direct problem numerical model, corresponding experimental data, and optimization algorithm. Experimental data revealing the recrystallization fraction evolution during heating of the deformed samples are extracted from extensive scanning electron microscopy (SEM) analysis. To distinguish between recrystallized and unrecrystallized grains, electron back scattered diffraction (EBSD) capabilities are used. The goal function is based on the square root error between measured and calculated recrystallization fractions as well as final grain sizes. Finally, the minimization of the defined goal function is based on the nature-inspired technique. As a result of the inverse approach, a set of identified model coefficients for the SRX simulations for the commonly used in the industry ferritic-pearlitic steel is provided. Examples of microstructure evolution under heat treatment conditions are also presented to highlight model predictive capabilities.