ANN-based Predictions of Steel Hardenability utilizing CALPHAD data

In this presentation, a general artificial neural network (ANN) model is introduced for predicting microstructure formation and hardness in continuously cooled steels based on composition and austenitization condition. The model includes specialized sub-models for start and finish transformation temperatures, fractions of individual phases (ferrite, pearlite, bainite, and martensite), and hardness, as well as additional critical quantities. Thermo-Calc calculations and principles of physical metallurgy are integrated to enhance model accuracy and generalization. Additionally, a robust algorithm has been developed to enable rapid generation of Continuous Cooling Transformation (CCT) diagrams, detailing transformation temperatures and critical cooling rates, for any specific steel compositions and austenitization conditions. These predicted diagrams, along with phase fraction and hardness estimations, provide valuable guidance for optimizing steel heat treatments to achieve desired mechanical properties.