Steel Phase Kinetics Modeling using Symbolic Regression

TL;DR

This paper presents a symbolic regression approach to empirically model steel phase kinetics, generating differential equations from dilatometer data to predict phase transformations like ferrite, pearlite, and bainite.

Contribution

The paper introduces a novel method using symbolic regression and genetic programming to derive differential equations modeling steel phase kinetics from experimental data.

Findings

Successfully identified differential equations fitting phase transformation data

Predicted formation of ferrite, pearlite, and bainite phases

Model currently excludes martensite, with future plans to include it

Abstract

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.