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Dynamic recrystallization is being increasingly used to control the microstructure evolution in steels, either through a single pass deformation at high strains or through accumulation of strain over a number of deformations. The advantage of dynamic recrystallization is that it can produce very fine grains in the austenite that can then transform to very fine ferrite grains which in turn increases the strength and toughness of the final product. Also dynamic recrystallization has a strong effect on the flow curve, leading to much lower stresses at high strains than in its absence. Therefore, in the prediction of forging or rolling loads it is important to be able to accurately predict the occurrence and extent of dynamic recrystallization if accurate force predictions are required.
This project involves the development of a CA model for dynamic recrystallization which then utilizes a comprehensive data set for Type 304 austenitic stainless steel for validation. The model is validated over a wide range of temperatures and strain rates, including conditions where there is a large degree of grain refinement which requires repeated nucleation to form the typical necklace structure. The animation is showing the dislocation level on each recrystallizing grain from nucleation onset up to the full impinged microstructure.