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