@Article{macau_mods_00005879,
  author = 	{Jarren, Lukas C.
		and Gazenbiller, Eugen
		and Arya, Visheet
		and Reitz, R{\"u}diger
		and Oechsner, Matthias
		and Feiler, Christian
		and Zheludkevich, Mikhail L.
		and H{\"o}che, Daniel},
  title = 	{Machine Learning--Assisted Risk Assessment of Pitting Corrosion Susceptibility of AA1050 in Ethanol‐Containing Fuels},
  journal = 	{Materials and corrosion : Organ der GfKORR - Gesellschaft f{\"u}r Korrosionsschutz e.V., des Auskunftsdienstes Werkstoffberatung der DECHEMA e.V. und der Europ{\"a}ischen F{\"o}deration Korrosion = Werkstoffe und Korrosion},
  year = 	{2024},
  publisher = 	{Wiley},
  address = 	{Weinheim},
  volume = 	{76},
  number = 	{3},
  pages = 	{398--407},
  keywords = 	{aluminum; corrosion risk; decision support; pitting; supervised machine learning},
  abstract = 	{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.},
  issn = 	{0947-5117},
  doi = 	{10.1002/maco.202414598},
  url = 	{https://macau.uni-kiel.de/receive/macau_mods_00005879},
  url = 	{https://doi.org/10.1002/maco.202414598},
  file = 	{:https://macau.uni-kiel.de/servlets/MCRFileNodeServlet/macau_derivate_00007339/Materials%20Corrosion%20-%202024%20-%20Jarren%20-%20Machine%20Learning%20Assisted%20Risk%20Assessment%20of%20Pitting%20Corrosion%20Susceptibility%20of.pdf:PDF},
  language = 	{en}
}