000K utf8 1100 $c2024 1500 eng 2050 urn:nbn:de:gbv:8:3-2025-00348-3 2051 10.1002/maco.202414598 3000 Jarren, Lukas C. 3010 Arya, Visheet 3010 Feiler, Christian 3010 Gazenbiller, Eugen 3010 Höche, Daniel 3010 Oechsner, Matthias 3010 Reitz, Rüdiger 3010 Zheludkevich, Mikhail L. 4000 Machine Learning–Assisted Risk Assessment of Pitting Corrosion Susceptibility of AA1050 in Ethanol‐Containing Fuels$hWiley [Jarren, Lukas C.] 4030 $nWiley 4209 The ability to assess the risk of corrosion of metallic structures in particular environments holds considerable significance in the field of automotive industry. In recent years, machine learning has evolved into a crucial tool to evaluate the complex and multidimensional corrosion phenomena. In this paper, the special case of non-aqueous alcoholate pitting corrosion of AA1050 in ethanol-blended fuels with water and chloride contamination is examined via supervised machine learning techniques in order to distinguish between safe and unsafe conditions. The data space was created by conducting dedicated experiments with varying ethanol–fuel–water ratios, temperatures, and surface preparations. The classifier's performance rating of 0.87 (balanced accuracy) indicates an outstanding predictive ability and highlights the model's usefulness as decision support for subsequent experiments. 4950 https://doi.org/10.1002/maco.202414598$xR$3Volltext$534 4950 https://nbn-resolving.org/urn:nbn:de:gbv:8:3-2025-00348-3$xR$3Volltext$534 4961 https://macau.uni-kiel.de/receive/macau_mods_00005879 5051 600 5550 aluminum 5550 corrosion risk 5550 decision support 5550 pitting 5550 supervised machine learning