Development of Predictive Models for Tempering Behavior in Low-Carbon Bainitic Steel Using Integrated Tempering Parameters

Abstract

Low-carbon bainitic steels are known for their excellent combination of strength and toughness, making them suitable for various industrial applications. Understanding the tempering behavior of these steels is crucial for optimizing their mechanical properties through heat treatment. This study presents predictive models for tempering behavior based on empirical data, which is fundamental for understanding the thermal stability and transformation kinetics of the steel. Through integrated tempering parameters, we established predictive models that integrate tempering temperature and time, yielding a robust framework for predicting hardness. The equivalent tempering kinetic curves and nomographs plotted in this study allow for the direct determination of hardness under various tempering conditions, facilitating the optimization of tempering parameters. The nomogram approach provides a practical method for adjusting tempering parameters to achieve desired mechanical properties efficiently. The accuracy of the predictive models was validated through statistical tests, demonstrating a high correlation between predicted and experimental values.