Steel Phase Kinetics Modeling using Symbolic Regression
This work addresses the need for empirical modeling of steel phase transformations, which is incremental as it builds on existing symbolic regression methods and is limited to a single steel type without martensite.
The researchers tackled the problem of modeling steel phase kinetics by using symbolic regression and genetic programming to derive compact differential equations from dilatometer data, resulting in a model that predicts ferrite, pearlite, and bainite formation for a single steel type.
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.