Analysis of the elemental effects on the surface potential of aluminum alloy using machine learning

Autor:	Yuya Takara, Takahiro Ozawa, Masaki Yamaguchi
Rok vydání:	2022
Předmět:	Physics and Astronomy (miscellaneous) General Engineering General Physics and Astronomy
Zdroj:	Japanese Journal of Applied Physics. 61:SL1008
ISSN:	1347-4065 0021-4922
DOI:	10.35848/1347-4065/ac5a2a
Popis:	Aluminum alloy contains intermetallic compounds, which contribute to the improvement of strength properties. However, when it is exposed a to a corrosive environment, the area around the compounds is dissolved preferentially, resulting in the formation of pitting corrosion. Although this dissolution reaction is presumed to be caused by the potential difference (ΔV) between the matrix and the compounds, it has not been quantitatively clarified how ΔV is generated. In this article, we present our study on the effects of the compound composition on ΔV by using the technique of machine learning. The results showed that ΔV and the elemental concentration of the compounds have a linear relationship.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::646b79a3a96fc2daf59a1b95728cbd4c https://doi.org/10.35848/1347-4065/ac5a2a Zobrazit plný text záznamu