Steel Phase Kinetics Modeling using Symbolic Regression
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- @InProceedings{Piringer:2022:SYNASC,
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author = "David Piringer and Bernhard Bloder and
Gabriel Kronberger",
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booktitle = "2022 24th International Symposium on Symbolic and
Numeric Algorithms for Scientific Computing (SYNASC)",
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title = "Steel Phase Kinetics Modeling using Symbolic
Regression",
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year = "2022",
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pages = "327--330",
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abstract = "We describe an approach for empirical modelling 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
generalise to multiple steel types with different
chemical compositions.",
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keywords = "genetic algorithms, genetic programming, Phase
measurement, Scientific computing, Computational
modelling, Differential equations, Predictive models,
Prediction algorithms, Mathematical models, steel,
phase kinetics, symbolic regression, dynamic models",
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DOI = "doi:10.1109/SYNASC57785.2022.00059",
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ISSN = "2470-881X",
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month = sep,
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notes = "Also known as \cite{10131020}",
- }
Genetic Programming entries for
David Piringer
Bernhard Bloder
Gabriel Kronberger
Citations