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