@inproceedings{85561c25c5594d159cb1923837b79a52,
title = "Steel Phase Kinetics Modeling using Symbolic Regression.",
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.",
keywords = "dynamic models, genetic programming, phase kinetics, steel, symbolic regression",
author = "David Piringer and Bernhard Bloder and Gabriel Kronberger",
note = "Funding Information: The authors gratefully acknowledge the financial support under the scope of the COMET program within the K2 Center Integrated Com- putational Material, Process and Product Engineering (IC-MPPE) (Project No 886385). This program is supported by the Austrian Fed- eral Ministries for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK) and for Digital and Economic Affairs (BMDW), represented by the Austrian Research Promotion Agency (FFG), and the federal states of Styria, Upper Austria and Tyrol. Funding Information: The authors gratefully acknowledge the financial support under the scope of the COMET program within the K2 Center “Integrated Computational Material, Process and Product Engineering (IC-MPPE)” (Project No 886385). This program is supported by the Austrian Federal Ministries for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK) and for Digital and Economic Affairs (BMDW), represented by the Austrian Research Promotion Agency (FFG), and the federal states of Styria, Upper Austria and Tyrol. Publisher Copyright: {\textcopyright} 2022 IEEE.",
year = "2022",
doi = "10.1109/SYNASC57785.2022.00059",
language = "English",
isbn = "978-1-6654-6546-5",
series = "Proceedings - 2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2022",
publisher = "IEEE",
pages = "327--330",
editor = "Bruno Buchberger and Mircea Marin and Viorel Negru and Daniela Zaharie",
booktitle = "Proceedings - 2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2022",
}