论文标题
使用符号回归的钢相动力学建模
Steel Phase Kinetics Modeling using Symbolic Regression
论文作者
论文摘要
我们描述了一种基于符号回归和遗传编程的钢相动力学经验模型的方法。该算法采集从扩展计测量值收集的处理数据，并产生一个模拟相动力学的微分方程系统。我们的最初结果表明，所提出的方法允许识别适合数据的紧凑型微分方程。该模型可以预测单钢类型的铁氧体，珍珠岩和贝氏体形成。 Martensite尚未包含在模型中。未来的工作应结合马氏体，并推广到具有不同化学成分的多种钢类类型。
We describe an approach for empirical modeling of steel phase kinetics based on symbolic regression and genetic programming. The algorithm takes processed data gathered from dilatometer measurements and produces a system of differential equations that models the phase kinetics. Our initial results demonstrate that the proposed approach allows to identify compact differential equations that fit the data. The model predicts ferrite, pearlite and bainite formation for a single steel type. Martensite is not yet included in the model. Future work shall incorporate martensite and generalize to multiple steel types with different chemical compositions.
