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Predicting the parabolic growth rate constant for high-temperature oxidation of steels using machine learning models

Journal Article (2023)
Author(s)

Soroush Aghaeian (TU Delft - Team Amarante Bottger, TU Delft - Design for Sustainability)

Farshid Norouzi (TU Delft - DC systems, Energy conversion & Storage)

W.G. Sloof (TU Delft - Team Joris Dik)

J. M.C. Mol (TU Delft - Team Arjan Mol)

Amarante Böttger (TU Delft - Team Amarante Bottger)

Research Group
Design for Sustainability
Copyright
© 2023 S. Aghaeian, F. Norouzi, W.G. Sloof, J.M.C. Mol, A.J. Bottger
DOI related publication
https://doi.org/10.1016/j.corsci.2023.111309
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 S. Aghaeian, F. Norouzi, W.G. Sloof, J.M.C. Mol, A.J. Bottger
Research Group
Design for Sustainability
Volume number
221
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

The parabolic growth rate constant (kp) of high-temperature oxidation of steels is predicted via a data analytics approach. Four machine learning models including Artificial Neural Networks, Random Forest, k-Nearest Neighbors, and Support Vector Regression are trained to establish the relations between the input features (composition and temperature) and the target value (kp). The models are evaluated by the indices: Mean Absolute Error, Mean Squared Error, Root Mean Squared Error and Coefficient of Determination. The steel composition regarding Cr and Ni content and the temperature were the most significant input features controlling the oxidation kinetics.