ScholarWorks@경상국립대학교

초록

This study presents the enhancement of a phenomenological model for predicting grain size evolution during the hot deformation of SCR420HB bearing steel. The model now incorporates strain rate and temperature as controllable variables, thereby improving prediction accuracy for various combinations of these factors. Material's microstructural constants, including initial grain size exponent, strain exponent, strain rate exponent, and dynamic recrystallization activation energy, were determined through FEM-coupled optimization techniques. Accurate flow stress data, crucial for determining the onset of dynamic recrystallization, was integrated to enhance the model's precision. The accuracy of the optimized model was validated by comparing predicted and experimental grain sizes across different stages of the hot forging process, demonstrating improved model performance. Additionally, the study provides insights into the sensitivity of the grain size to different deformation conditions, offering valuable guidance for industrial forging applications. This comprehensive approach ensures the model's robustness and practical applicability in real-world scenarios. © 2024 Elsevier Ltd

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