Machine learning based prediction of the corrosion behaviour of biodegradable magnesium alloys with PEO coatings

The present work focuses on the application of deep learning methods for predicting the in vitro pitting corrosion behaviour of coated magnesium alloys with different microstructures. The datasets are generated by phase field simulation of pitting corrosion. Performance analysis shows that physics-informed convolutional neural network (CNN) is superior to regular CNN in predicting corrosion curve. A good agreement between deep learning predictions and the ground truths indicates excellent predictive performance of the current encoder-decoder architecture regardless of the variation of microstructures. Furthermore, the computational time is reduced by 4 to 6 orders of magnitude compared to the phase field simulation. The computational results demonstrate that machine learning based approach is promising alternative to traditional modelling methods for predicting pitting corrosion of coated magnesium alloys.