4.7 Article

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

Journal

CORROSION SCIENCE
Volume 225, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.corsci.2023.111619

Keywords

Carbon steel; Weight loss; Atmospheric corrosion

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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.
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.

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