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Free keywords:
ABAQUS; Austenitic stainless steel; Cellular automata; Constitutive models; Deformation; Drag; Dynamic recrystallization; Grain growth; Growth kinetics; Microstructure; Neural networks; Strain hardening; Strain rate, C-models; Cellular automata models; Cellular automatons; Correlation coefficient; Deformation conditions; Dynamic recrystallisation; Flow behaviours; Isothermal deformation; Neural-networks; Super-austenitic stainless steels, Finite element method
Abstract:
A cellular automaton (CA) model for dynamic recrystallization (DRX) is established by employing Moore's neighboring rule to predict flow stress, DRX grain size (DDRX) and DRX fraction (XDRX). The CA model has been optimized for super austenitic stainless steel at different strain rates (0.001–10 s−1) and temperatures (1173–1423 K) under isothermal deformation conditions. The output of the CA simulation has been used for establishing ANN-based constitutive models. The trained ANN-based constitutive models have been further implemented in FEM software (ABAQUS 6.14) to evaluate flow behavior and microstructure response of the alloy under various non-isothermal deformation conditions. The conventional CA (CAC) model has failed to provide a good depiction of the microstructure evolution, as it revealed a very low correlation coefficient (R) for XDRX (R ~ 0.75) and DDRX (R ~ 0.8). This inaccuracy of the model could be related to its inherent inability to consider the effect of solute drag on grain growth and DRX kinetics. Therefore, a modified cellular automata (CAM) model has been developed by introducing a new temperature-strain rate-dependent mobility parameter for numerically considering the solute drag effect. Employing non-isothermal simulations, the CAM model has revealed a higher correlation coefficient than the CAC model for predicting XDRX (R ~ 0.95) and DDRX (R ~ 0.98). Moreover, the developed CAM model has also predicted the flow behavior of the alloy in the entire domain investigated, revealing a higher correlation coefficient (R ~ 0.987) and a low average absolute relative error (8.6). © 2021 Elsevier B.V.
