PT Journal AU Jarren, LC Gazenbiller, E Arya, V Reitz, R Oechsner, M Feiler, C Zheludkevich, ML Höche, D TI Machine Learning–Assisted Risk Assessment of Pitting Corrosion Susceptibility of AA1050 in Ethanol‐Containing Fuels SO Materials and corrosion : Organ der GfKORR - Gesellschaft für Korrosionsschutz e.V., des Auskunftsdienstes Werkstoffberatung der DECHEMA e.V. und der Europäischen Föderation Korrosion = Werkstoffe und Korrosion JI Materials & Corrosion PY 2024 BP 398 EP 407 VL 76 IS 3 PU Wiley DI 10.1002/maco.202414598 WP https://macau.uni-kiel.de/receive/macau_mods_00005879 LA en DE aluminum; corrosion risk; decision support; pitting; supervised machine learning SN 0947-5117 AB 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. PI Weinheim ER