Prediction of pitting corrosion status of EN 1.4404 stainless steel by using a 2‐stage procedure based on support vector machines

Abstract

AbstractThe excellent properties of EN 1.4404 have made this material one of the most popular types of austenitic stainless steel used for many applications. However, in aggressive environments, this alloy may suffer corrosion. Electrochemical analyses have been extensively used in order to evaluate pitting corrosion behaviour of stainless steel. These techniques may be followed by microscopic analysis in order to determine the resistance of the passive layer. This step requires the human interpretation, and therefore, subjectivity may be included in the results. This work aims to solve this drawback by the development of an automatic model with the capability to predict pitting corrosion status of this material. A combined model based on support vector machines (SVMs) is presented in this work. With the aim to improve the prediction performance, the model considers the breakdown potential values estimated by itself at a first stage. The performance is evaluated based on receiver operating characteristic (ROC) curves. The area under the curve (AUC) and accuracy results (0.998 and 0.952, respectively) demonstrate the utility of the proposed model as an efficient and accurate tool to predict pitting behaviour of EN 1.4404 automatically.