Samenvatting

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 ‘Machine learning for corrosion database’ 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.

Originele taal-2English
TitelReviewing machine learning of corrosion prediction in a data-oriented perspective
Uitgeverijnpj materials degradation
Pagina's56
Aantal pagina's72
Volume6
Uitgave1
DOI's
StatusPublished - dec 2022
EvenementEurCorr 2022 - Berlin, Germany
Duur: 28 aug 20221 sep 2022
https://eurocorr.org/2022.html

Publicatie series

Naamnpj Materials Degradation
UitgeverijNature Research
ISSN van geprinte versie2397-2106

Conference

ConferenceEurCorr 2022
Land/RegioGermany
StadBerlin
Periode28/08/221/09/22
Internet adres

Bibliografische nota

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:
© 2022, The Author(s).

Copyright:
Copyright 2022 Elsevier B.V., All rights reserved.

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