Application of machine learning in predicting pitting corrosion - electrochemical data around the open circuit potential

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.