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<p>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.</p>

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Surrogate modelling of corrosion inhibition finite element simulations using machine learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月26日

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  • Lisa Sahlmann,
  • Nourhan Abdelrahman,
  • Mats Meeusen,
  • Koen Delaere,
  • Peter Meuris,
  • Bart Van den Bossche,
  • Natalia Konchakova,
  • Herman Terryn,
  • Mesfin Haile Mamme,
  • Christian Feiler,
  • Mikhail L. Zheludkevich

摘要

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.