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

• Published in: Materials Vol. 16; no. 19; p. 6583
• Main Authors: Razali, Mohd Kaswandee, Abd Ghawi, Afaf Amera, Irani, Missam, Chung, Suk Hwan, Choi, Jeong Muk, Joun, Man Soo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2023; MDPI
• Subjects: Accuracy; Adiabatic conditions; Alloys; Austenite; austenite grain growth (AGG); Boron steel; Case studies; generalized reduced gradient (GRG) optimization; Grain growth; Grain size; Growth models; Heat treatment; Hot rolling; isothermal heat treatment; Low alloy steels; Material properties; Mathematical models; Mechanical properties; Metal forming; Nuclear energy; Nuclear power plants; Optimization; Optimization techniques; Parameters; Q R; Regression analysis; Specialty steels; Steel alloys; Temperature; South Korea
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma16196583

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.