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The breakdown potential is a crucial factor in the study of pitting corrosion resistance of stainless steel. This work aims to demonstrate the advantage of different chemometric techniques to estimate the breakdown potential of austenitic stainless steel. In order to predict pitting corrosion behaviour of stainless steel, a total of 60 samples of this alloy were subjected to electrochemical tests varying chloride ion concentration, pH and temperature. The experimental values of the breakdown potential, in addition to the tested environmental factors, were used to construct the predictive models based on support vector machines and artificial neural networks. A multiple-comparison study based on statistic tests was applied to determine the optimal configuration for each technique. According to the results, support vector machines became a suitable and reliable technique to be applied in the modelling of the breakdown potential of austenitic stainless steels. This technique outperformed the models based on artificial neural networks and provided a useful tool to compare the pitting corrosion resistance of stainless steel in different environmental conditions without recourse to polarization tests. Therefore, this model presented a relevant meaning in science and engineering applications. /journal/cem and Fisher LSD tests with a significance level of α = 0.05 using 0.632 bootstrap as validation technique.Breakdown potential modelling

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