, Leonardo Bertolucci Coelho, Yves Van Ingelgem, Herman Terryn, Ann Nowe, Denis Steckelmacher, Dawei Zhang
This work provides a data-oriented overview of the rapidly growing research field covering machine learning (ML) applied to predicting electrochemical corrosion. Our main aim was to determine which ML models have been applied and how well they performed depending on the corrosion topic considered. From an extensive review of corrosion articles presenting comparable performance metrics, a {\textquoteleft}Machine learning for corrosion database{\textquoteright} was created, guiding corrosion experts and model developers in their applications of ML to corrosion. Potential research gaps and recommendations are discussed, and a broad perspective for future research paths is provided.
Vangrunderbeek, V, Bertolucci Coelho, L, Van Ingelgem, Y, Terryn, H, Nowe, A, Steckelmacher, D & Zhang, D 2022, Reviewing machine learning of corrosion prediction in a data-oriented perspective. in Reviewing machine learning of corrosion prediction in a data-oriented perspective. 1 edn, vol. 6, npj Materials Degradation, npj materials degradation, pp. 56-72, EurCorr 2022, Berlin, Germany, 28/08/22. https://doi.org/10.1038/s41529-022-00218-4
Vangrunderbeek, V., Bertolucci Coelho, L., Van Ingelgem, Y., Terryn, H., Nowe, A., Steckelmacher, D., & Zhang, D. (2022). Reviewing machine learning of corrosion prediction in a data-oriented perspective. In Reviewing machine learning of corrosion prediction in a data-oriented perspective (1 ed., Vol. 6, pp. 56-72). (npj Materials Degradation). npj materials degradation. https://doi.org/10.1038/s41529-022-00218-4
@inproceedings{2c1c08dfb9b9487796cd819028288af6,
title = "Reviewing machine learning of corrosion prediction in a data-oriented perspective",
abstract = "This work provides a data-oriented overview of the rapidly growing research field covering machine learning (ML) applied to predicting electrochemical corrosion. Our main aim was to determine which ML models have been applied and how well they performed depending on the corrosion topic considered. From an extensive review of corrosion articles presenting comparable performance metrics, a {\textquoteleft}Machine learning for corrosion database{\textquoteright} was created, guiding corrosion experts and model developers in their applications of ML to corrosion. Potential research gaps and recommendations are discussed, and a broad perspective for future research paths is provided.",
author = "Vincent Vangrunderbeek and {Bertolucci Coelho}, Leonardo and {Van Ingelgem}, Yves and Herman Terryn and Ann Nowe and Denis Steckelmacher and Dawei Zhang",
note = "Funding Information: The author L.B. Coelho is a Postdoctoral Researcher of the Fonds de la Recherche Scientifique – FNRS which is gratefully acknowledged. Publisher Copyright: {\textcopyright} 2022, The Author(s). Copyright: Copyright 2022 Elsevier B.V., All rights reserved.; EurCorr 2022 ; Conference date: 28-08-2022 Through 01-09-2022",
year = "2022",
month = dec,
doi = "10.1038/s41529-022-00218-4",
language = "English",
volume = "6",
series = "npj Materials Degradation",
publisher = "npj materials degradation",
pages = "56--72",
booktitle = "Reviewing machine learning of corrosion prediction in a data-oriented perspective",
edition = "1",
url = "https://eurocorr.org/2022.html",
}