Pitting corrosion behaviour of austenitic stainless steel using artificial intelligence techniques

AbstractDifferent artificial intelligent tools have been used to model pitting corrosion behaviour of EN 1.4404 austenitic stainless steel. Samples from this material have been subjected to polarization tests in different chloride solutions using different precursor salts: NaCl and MgCl2. The aim of this work is to compare the results obtained from the different classification models using both solutions studying the influence of them. Furthermore, in order to determine pitting potential values (Epit), different environmental conditions have been tested varying chloride ion concentration, pH value and temperature. The techniques used try to find the relation between the environmental parameters studied and the status pitting corrosion of this alloy. Several classification techniques have been used: Classification Trees (CT), Discriminant Analysis (DA), K-Nearest-Neighbours (K-NN), Back-Propagation Neural Networks (BPNN) and Support Vector Machine (SVM). The results obtained show the good correlation between experimental and predicted data for all the cases studied demonstrating the utility of artificial intelligence for modelling pitting corrosion problem