Application of machine learning in predicting pitting corrosion – electrochemical data around the open circuit potential
Anti-Corrosion Methods and Materials
ISSN: 0003-5599
Article publication date: 17 March 2022
Issue publication date: 14 April 2022
Abstract
Purpose
The purpose of this study is to confirm the idea that observing the electrochemical data of a steel polarized around its open circuit potential can provide insight into its performance against pitting corrosion. To confirm this idea a two-step work was carried out. The authors collected electrochemical data through experiments and exploited them through machine learning by building neural networks capable of predicting the behaviour of the steel against the pitting corrosion.
Design/methodology/approach
The electrochemical experiments consist in plotting voltammograms of the steel in chemical solutions of various degrees of corrosiveness. For each experiment, the authors observe how the open-circuit potential evolves over a period of 1 min, and following this, the authors observe the current evolution when they impose a potential scan that starts from the open-circuit potential. For each of these situations, the pitting potential Epit is noted. The authors then build different artificial neural networks, which after learning, can, by receiving electrochemical data, calculate a pitting potential Epit′. The performance of the neural networks is evaluated by the correlation of Epit and Epit′.
Findings
Through this work, different types of networks were compared. The results show that recurrent or convolutional networks can better capture the temporal nature of the input data.
Originality/value
The results of this work support the idea that the measurable electrochemical data around the free potential of a material can be correlated with its behaviour at more anodic potentials, particularly the initiation of pits.
Keywords
Acknowledgements
This work was carried out with the resources of the Research and Development Unit of Nuclear Engineering, URDIN. The data that allowed its realization were obtained at the Nuclear Research Centre of Birine. We thank all the people who contributed to its realization.
Data availability: The raw/processed data required to reproduce these findings cannot be shared at this time because of legal or ethical reasons.
Citation
Boucherit, M.N., Amzert, S.A. and Arbaoui, F. (2022), "Application of machine learning in predicting pitting corrosion – electrochemical data around the open circuit potential", Anti-Corrosion Methods and Materials, Vol. 69 No. 3, pp. 295-301. https://doi.org/10.1108/ACMM-07-2021-2516
Publisher
:Emerald Publishing Limited
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