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A support vector machine‐based ensemble algorithm for pitting corrosion modeling of EN 1.4404 stainless steel in sodium chloride solutions
Author(s) -
JiménezCome María Jesús,
de la Luz Martín María,
Matres Victoria
Publication year - 2019
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.201810367
Subject(s) - pitting corrosion , corrosion , materials science , chloride , support vector machine , metallurgy , sensitivity (control systems) , sodium , computer science , engineering , artificial intelligence , electronic engineering
The resistance to localized corrosion of stainless steel in sodium chloride solutions is evaluated at different conditions. The effects of chloride ion concentration, acidity, and temperature on pitting corrosion resistance are analyzed. In order to develop a model capable of predicting pitting corrosion behavior of EN 1.4404 by an automatic way, a support vector machine‐based ensemble algorithm is proposed. An additional step related to feature selection is included in the model in order to improve the prediction capability. According to the excellent prediction results, the support vector machines (SVM) model is used to perform a sensitivity analysis for the purpose of analyzing the influence of the environmental variables on the breakdown potential and the pitting corrosion status modeling of this grade of stainless steel. Based on this analysis, it can be concluded that the breakdown potential is the main variable to be considered in pitting corrosion modeling whereas temperature is the most important one for breakdown potential modeling.