Determining the Effect of the Main Alloying Elements on Localized Corrosion in Nickel Alloys Using Artificial Neural Networks

Abstract

Nickel base alloys are considered among candidate materials for engineered barriers of nuclear repositories. The localized corrosion resistance is a determining factor in materials selection for this application. This work compares the crevice corrosion resistance of several commercial nickel base alloys using artificial neural networks. The crevice corrosion repassivation potential of the tested alloys was determined by the potentiodynamic-galvanostatic-potentiodynamic (PD-GS-PD) method. The testing temperature was 60ªC and the chloride concentrations used were 0,1M, 1M and 10M. The results indicate that the repassivation potential increases linearly with the PREN (Pitting Resistant Equivalent Number) at high chloride concentrations. We also found a linear relationship between the repassivation potential and the logarithm of the concentration of chloride. Analysis from artificial neural networks presents distinctive patterns between the mayor alloying components and the chloride concentration and the repassivation potential. Predictions from artificial neural networks fit with successive tested commercial nickel alloys.