Predicting Corrosion Behaviour of Magnesium Alloy Using Machine Learning Approaches

The primary objective of this study is to develop a machine learning-based predictive model using corrosion rate data for magnesium alloys compiled from the literature. Corrosion rates measured under different deformation rates and heat treatment parameters were analyzed using artificial intelligence algorithms. Variables such as chemical composition, heat treatment temperature and time, deformation state, pH, test method, and test duration were used as inputs in the dataset. Various regression algorithms were compared with the PyCaret AutoML library, and the models with the highest accuracy scores were analyzed with Gradient Extra Trees and AdaBoost regression methods. The findings of this study demonstrate that modelling corrosion behaviour by integrating chemical composition with experimental conditions and processing parameters substantially enhances predictive accuracy. The regression models, developed using the PyCaret library, achieved high accuracy scores, producing corrosion rate predictions that are remarkably consistent with experimental values reported in the literature. Detailed tables and figures confirm that the most influential factors governing corrosion were successfully identified, providing valuable insights into the underlying mechanisms. These results highlight the potential of AI-assisted decision systems as powerful tools for material selection and experimental design, and, when supported by larger databases, for predicting the corrosion life of magnesium alloys and guiding the development of new alloys.