Rapid machine learning based corrosion monitoring and prediction for a sustainable life cycle of aerospace materials

Corrosion inevitably disintegrates structural materials resulting in major environmental impact by increased material consumption; and the aviation sector is no exception. Especially the corrosion of aluminum parts is a major concern, and more recently also degradation of composite materials plays a central role for sustainable aviation industries. A US Air Force (USAF) report indicated that corrosion accounted for almost a quarter of the total maintenance costs and 15% of total hours of unavailability. Very recently and still not solved, delamination of paint from composite materials at a highly accelerated rate, in particular from embedded copper structures for lightning strike protection of the A350, leads to exposure of the composite structure underneath to severe corrosion. These two examples highlight the utmost importance of corrosion and delamination protection as well as early detection of corrosive damage. This project will develop a rapid machine learning based corrosion monitoring and prediction for a sustainable life cycle of aerospace materials.

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UWK

Konsortium: SenZoro GmbH (Wien), Wien Energie (Wien), CEST (NÖ), UWK (NÖ), JKU (OÖ), RoRa (OÖ)

Förderung im TakeOff-Call der FFG, FFG-Nr: 903054