Abstract 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.

中文翻译：

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通过膨胀测量对铁素体转变动力学进行贝叶斯推断

摘要 提出了一种贝叶斯方法，用于阐明仅从膨胀曲线来解释低碳钢在不同连续冷却条件下的转变动力学的最佳动力学模型。为了估计动力学参数以及候选动力学模型的模型合理性，使用了交换马尔可夫链蒙特卡罗方法。通过对 Fe-0.15C-1.5Mn 合金中铁素体形成的金相研究证明了所提出方法的有效性。结果表明，该方法已成功应用于阐明铁素体转变动力学，例如转变开始温度、形成机制和微观结构的分数。与之前的实验研究相比，