Analytical models have been developed for the transformation kinetics, microstructure analysis and the mechanical properties in bainitic steels. Three models are proposed for the bainitic transformation based on the chemical composition and the heat treatment conditions of the steel as inputs: (1) thermodynamic model on kinetics of bainite transformation, (2) improved thermo-statistical model that eliminates the material dependent empirical constants and (3) an artificial neural network model to predict the volume fraction of bainite. Neural networks have also been used to model the hardness of high carbon steels, subjected to isothermal heat treatment. Collectively, for a steel of given composition and subjected to a particular isothermal heat treatment, the models can be used to determine the volume fraction of bainitic phase and the material hardness values.
The models have been extensively validated with the experimental data from literature as well as from three new high carbon experimental steels with various alloying elements that
were used in the present work. For these experimental steels, data on the volume fraction of phases (via X-ray diffraction), yield strength (via compression tests) and hardness were obtained for various combinations of isothermal heat treatment times and temperatures. The heat treated steels were subjected to compression and hardness tests and the data have been used to develop a new correlation between the yield stress and the hardness. It was observed that while all three experimental steels exhibit a predominantly nanostructured bainite microstructure, the presence of Co and Al in one of the steels accelerated and maximized the nano-bainitic transformation within a reasonably short isothermal transformation time. Excellent yield strength (>1.7 GPa) and good deformability were observed in this steel after isothermal heat treatment at a low temperature of 250C for a relatively short duration of 24 hours.
Modeling And Characterization Of High Carbon Nanobainitic Steels