Abstract
Different methods of classification have been used in this paper to model pitting corrosion behaviour of austenitic stainless steel EN 1.4404. This material was subjected to electrochemical polarization tests in aqueous environment of varying chloride ion concentration (from NaCl solutions), pH values and temperature in order to determine values of critical pitting potentials (Epit) for each condition tested. In this way, the classification methods employed try to simulate the relation between Epit and those various environmental parameters studied. Different techniques have been used: Classification Trees (CT), Discriminant Analysis (DA), K-Nearest-Neighbours (K-NN), Backpropagation Neural Networks (BPNN) and Support Vector Machine (SVM). These models have generally been regarded as successful. They have been able to give a good correlation between experimental and predicted data. The analysis of the results becomes useful to plan improvement in the austenitic stainless steel protection and to avoid critical conditions expositures of this material.
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Jiménez-Come, M.J. et al. (2011). Austenitic Stainless Steel EN 1.4404 Corrosion Detection Using Classification Techniques. In: Corchado, E., Snášel, V., Sedano, J., Hassanien, A.E., Calvo, J.L., Ślȩzak, D. (eds) Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011. Advances in Intelligent and Soft Computing, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19644-7_21
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DOI: https://doi.org/10.1007/978-3-642-19644-7_21
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