ABSTRACT The need for fast mean field models describing phase transformations in varying temperatures was the motivation for the research. The aim was to propose an austenite-ferrite transformation model which can be successfully substituted for the JMAK model and eliminate its weak points. The model consists of a second order differential equation which describes the kinetics of both the austenite-ferrite and the reverse transformations. The main advantage of such an approach is the ability to perform calculations for varying temperature without using the additivity rule. The coefficients in the equations were determined by performing the inverse analysis for the dilatometric data. The inverse approach was transformed into an optimisation task, which was solved using the Particle Swarm Optimization (PSO) algorithm. The first part of the work is devoted to the description of the model and is completed with the calculation of the coefficients obtained for two DP steels. The second part consists of numerical tests and the validation of the model, which was performed by simulations of two industrial thermal cycles. The final part of the work is devoted to the optimisation of the thermal cycle for continuous annealing, in order to obtain the required phase composition by changing parameters of the thermal cycle. This part also contains the results of sensitivity analysis of final phase distribution.

中文翻译：

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钢在连续退火线加热-冷却热循环下的相变模型

摘要 研究的动机是需要描述不同温度下的相变的快速平均场模型。目的是提出一种奥氏体-铁素体转变模型，该模型可以成功替代 JMAK 模型并消除其弱点。该模型由一个描述奥氏体-铁素体和逆相转变动力学的二阶微分方程组成。这种方法的主要优点是能够在不使用可加性规则的情况下对变化的温度进行计算。方程中的系数是通过对膨胀数据进行逆分析来确定的。逆方法转化为优化任务，使用粒子群优化 (PSO) 算法求解。工作的第一部分致力于模型的描述，并通过计算两种 DP 钢获得的系数来完成。第二部分包括数值测试和模型验证，该模型通过两个工业热循环的模拟进行。工作的最后一部分致力于优化连续退火的热循环，以便通过改变热循环参数来获得所需的相组成。这部分还包含最终相分布的灵敏度分析结果。工作的最后一部分致力于优化连续退火的热循环，以便通过改变热循环参数来获得所需的相组成。这部分还包含最终相分布的灵敏度分析结果。工作的最后一部分致力于优化连续退火的热循环，以便通过改变热循环参数来获得所需的相组成。这部分还包含最终相分布的灵敏度分析结果。