SummaryIn this study, a rigorous methodology for quantifying recrystallization kinetics by electron backscatter diffraction is proposed in order to reduce errors associated with the operator's skill. An adaptive criterion to determine adjustable grain orientation spread depending on the recrystallization stage is proposed to better identify the recrystallized grains in the partially recrystallized microstructure. The proposed method was applied in characterizing the microstructure evolution during annealing of interstitial-free steel cold rolled to low and high true strain levels of 0.7 and 1.6, respectively. The recrystallization kinetics determined by the proposed method was found to be consistent with the standard method of Vickers microhardness. The application of the proposed method to the overall recrystallization stages showed that it can be used for the rigorous characterization of progressive microstructure evolution, especially for the severely deformed material.

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Microstructure-based simulations were performed to understand the mechanism involved with texture formation during recrystallization in polycrystalline interstitial free (IF) steel. The crystal plasticity finite element method (CPFEM) was used to simulate mesoscopic deformation with its heterogeneity. The orientation components were decomposed according to the stored deformation energy, and the results were used to define potential candidates for nucleation sites. On the basis of the oriented nucleation approach, the subsequent evolution of microstructure and texture during recrystallization was simulated with the cellular automaton (CA) method. The coupled microstructure-based simulations provided the recrystallization kinetics, grain size distribution, and crystallographic texture of the recrystallized IF steels. Those results were in good agreement with experimental results obtained from electron back scattered diffraction (EBSD). This suggests that the level of detail of the deformed state captured from the crystal plasticity FE calculation can provide enough information, in terms of local stored energy and nucleation site selection, to enable modeling of the subsequent primary recrystallization process.

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