Exploring the potential of transfer learning in extrapolating accelerated corrosion test data for long-term atmospheric corrosion forecasting

This study utilizes transfer learning (TL) to enhance long-term atmospheric corrosion predictions. Using a Fe/Cu galvanic-type sensor, we gathered data in a controlled SAE J2334 salt spray setting and transferred this to an uncontrolled outdoor environment. Among TL methods tested, freezing the initial layer and fine-tuning others at a lower rate was most effective. The approach excelled at forecasting outdoor corrosion behaviour using a limited dataset. This approach could provide a solution to extrapolate results from controlled corrosion tests to unpredictable outdoor conditions and addressing data scarcity in machine learning modelling in the context of atmospheric corrosion.

Automated summary

This study applies transfer learning to improve long-term atmospheric corrosion predictions by transferring data from controlled environments to uncontrolled outdoor conditions. Among the tested transfer learning methods, freezing the initial layer and fine-tuning others at a lower rate showed the best results. This approach effectively forecasts outdoor corrosion behavior using a limited dataset, addressing data scarcity in machine learning models for atmospheric corrosion.