Transfer learning applications for corrosion prediction for electrodissoluted specimens

Corrosion monitoring is a critical aspect of Structural Health Monitoring (SHM), as it presents inherent challenges in tracking its progression and requires strategically placed sensors for effective surveillance. Moreover, the intricate interplay between environmental factors and the material composition of corroding components further complicates this phenomenon. In this study, a novel neural network framework is designed to predict corrosion evolution based on environmental data, focusing on three distinct aluminium alloys: Al7475, Al2024, and F357. Dedicated neural network models are developed for Al7475 and F357, trained on electrodissolution data, including process parameters and profilometry measurements. These models predict corrosion progression in terms of volume loss, corroded depth, and area based on charge measurements. To address the challenge of material-environment coupling, transfer learning is employed to adapt the neural network to Al2024. The oxidation charge passed during the electrodissolution mimics in an artificial way the corrosion process due to environmental conditions in the attack site. Consequently, the implemented neural network establishes a robust connection between corrosion process measurements and profilometry data, enabling accurate corrosion progression prediction based on oxidation charge. This framework allows the prediction of a component's residual useful life based on the assessment of the corrosion state, with the knowledge of volume loss further supporting mechanical assessment by evaluating resistant section or pit dimensions, ultimately aiding in the pit-crack transition analysis.