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Global sensitivity analysis of influence parameters in pitting corrosion behavior of 304 stainless steel using adaptive neuro‐fuzzy inference systems
Author(s) -
Xu Kaixin,
Sun Wen,
Wang Lida,
Yang Zhengqing,
Wang Jing,
Wang Suilin,
Liu Guichang
Publication year - 2021
Publication title -
materials and corrosion
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.487
H-Index - 55
eISSN - 1521-4176
pISSN - 0947-5117
DOI - 10.1002/maco.202012120
Subject(s) - adaptive neuro fuzzy inference system , pitting corrosion , inference system , corrosion , sensitivity (control systems) , fuzzy inference system , materials science , inference , fuzzy logic , metallurgy , computer science , engineering , fuzzy control system , artificial intelligence , electronic engineering
Pitting corrosion is simultaneously influenced by many parameters. However, conventional experimental methods do not offer information on the relative importance of each parameter to pitting corrosion. Herein, pitting corrosion of 304SS under multiple parameter influences was investigated by the global sensitivity analysis (GSA) based on adaptive neuro‐fuzzy inference system (ANFIS). It was applied for the first time in quantitatively analyzing the relative importance of each parameter to pitting corrosion. It was developed in two different systems, based on the experimental data and the published data, respectively. Both results show that ANFIS exhibits a highly accurate prediction result. The GSA based on ANFIS quantitatively analyzes the relative importance of the initiation of pitting corrosion of 304SS for each parameter, and based on the results of GSA, it can also be determined whether the parameter inhibits or promotes pitting corrosion. We believe this method could provide guidance for corrosion research.