Practical Approach for Determining Material Parameters When Predicting Austenite Grain Growth under Isothermal Heat Treatment

Abstract

An investigation of austenite grain growth (AGG) during the isothermal heat treatment of low-alloy steel is conducted. The goal is to uncover the effect of time, temperature, and initial grain size on SA508-III steel grain growth. Understanding this relationship enables the optimization of the time and temperature of the heat treatment to achieve the desired grain size in the studied steel. A modified Arrhenius model is used to model austenite grain size (AGS) growth distributions. With this model, it is possible to predict how grain size will change depending on heat treatment conditions. Then, the generalized reduced gradient (GRG) optimization method is employed under adiabatic conditions to characterize the model’s parameters, providing a more precise solution than traditional methods. With optimal model parameters, predicted AGS agree well with measured values. The model shows that AGS increases faster as temperature and time increase. Similarly, grain size grows directly in proportion to the initial grain size. The optimized parameters are then applied to a practical case study with a similar specimen size and material properties, demonstrating that our approach can efficiently and accurately predict AGS growth via GRG optimization.