Reviewing machine learning of corrosion prediction in a data-oriented perspective

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. F...

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Bibliographic Details
Published in:Npj Materials degradation 2022-01, Vol.6 (1), p.1-16, Article 8
Main Authors: Coelho, Leonardo Bertolucci, Zhang, Dawei, Van Ingelgem, Yves, Steckelmacher, Denis, Nowé, Ann, Terryn, Herman
Format: Article
Language:English
Subjects:
639/301/1034/1037
639/301/930/12
Chemistry and Materials Science
Corrosion
Corrosion and Coatings
Corrosion mechanisms
Corrosion potential
Electrochemical corrosion
Electrochemistry
Machine learning
Materials Science
Performance measurement
Review Article
Structural Materials
Tribology
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Summary: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.
ISSN:2397-2106
2397-2106
DOI:10.1038/s41529-022-00218-4