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

Journal article (2023)

Authors

S. Aghaeian Team Amarante Bottger - Mechanical, Maritime and Materials Engineering , Design for Sustainability - IDE
F. Norouzi DC systems, Energy conversion & Storage - EEMCS
W.G. Sloof Team Joris Dik - Mechanical, Maritime and Materials Engineering
J.M.C. Mol Team Arjan Mol - Mechanical, Maritime and Materials Engineering
A.J. Bottger Team Amarante Bottger - Mechanical, Maritime and Materials Engineering
Research Group
Design for Sustainability (IDE) (TU Delft)
Copyright: © 2023 S. Aghaeian, F. Norouzi, W.G. Sloof, J.M.C. Mol, A.J. Bottger
DOI: https://doi.org/10.1016/j.corsci.2023.111309
More Info
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Published Date

2023

Language

English

Faculty

Industrial Design Engineering

Department

Sustainable Design Engineering

Research Group

Design for Sustainability

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.