Bayesian inference of ferrite transformation kinetics from dilatometric measurement

A Bayesian approach is presented for clarifying the best kinetic model explaining the transformation kinetics of a low-carbon steel under different continuous cooling conditions only from dilatometric curves. To estimate kinetic parameters as well as the model plausibility of candidate kinetic models, the exchange Markov chain Monte Carlo method was used. The effectiveness of the proposed method was demonstrated by metallographic investigations of the ferrite formation in a Fe-0.15C-1.5Mn alloy. It is shown that the method is successfully applied for clarifying ferrite transformation kinetics, such as transformation start temperatures, formation mechanisms, and fractions of microstructures. In comparison with a previous experimental study, it is also presented that the important parameter determining the ferrite nucleation rate can be estimated only from dilatometric curves without the help of intensive metallographic observations.