Abstract
Passivation is a chemical process in which the electrochemical condition of passivity is gained on the surface of metal alloys. Biomedical AISI 316LVM stainless steel (SS) can be passivized by means of nitric acid immersion in order to improve a protective oxide layer on the surface and consequently increase corrosion resistance of the SS in the physiological solutions. In this study, multiple regression analysis and artificial neural network (ANN) were employed for mathematical modeling of the AISI 316LVM SS passivation process after immersion in the nitric acid solution. The pitting potential, which represents the mea-sure of pitting corrosion resistance, was chosen as the response, while the passivation parameters were nitric acid concentration, temperature and passivation time. The comparison between experimental results and models predictions showed that only the ANN model provided statistically accurate predictions with a high coefficient of determination and a low mean relative error. Finally, based on the derived ANN equation, the effects of the passivation parameters on pitting potential were examined.
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