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Abstract A modelling approach that combines a previously developed 2D continuum finite element model with machine learning to support the design and evaluation of corrosion-inhibiting coatings. The FEM simulates the leaching of corrosion inhibition pigments from an organic coating and the resulting protection of the metal surface. This is conducted for a system of aluminium alloy 2024-T3 with an active protective coating loaded with lithium carbonate particles. A generated dataset from FEM results was used to train ML models to predict inhibitor concentration and corrosion current density based on geometric and material input parameters. A feature importance analysis was conducted to identify the most influential input variables, providing insight into the factors controlling the achievement of corrosion inhibition. Furthermore, a blind test was performed using five unseen cases that were not involved in the training phase. Finally, the trained models were applied to explore their use in coating design.
Sahlmann et al. (Thu,) studied this question.