Austenitic Stainless Steel EN 1.4404 Corrosion Detection Using Classification Techniques
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.
Keywordsaustenitic stainless steel pitting corrosion classification methods SVM
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- 1.Sedriks, A.J.: Corrosion of Stainless Steel. John Wiley and Sons, West Sussex (1996)Google Scholar
- 4.Wei, Y., Yaxiu, L.: Fourth International Conference on Natural Computation Predicting the Corrosion Rates of Steels in Sea Water Using Artificial Neural Network (2008), doi:10.1109/ICNC.2008.481Google Scholar
- 9.Alfonsson, E., Quarfort, R.: Investigation of the applicability of some PRE expression for austenitic stainless steels. Avesta Corrosion Management 1, 1–5 (1992)Google Scholar
- 10.Corchado, E., Herrero, A.: Neural visualization of network traffic data for intrusion detection. Applied Soft Computing (2010), doi:10.1016/j.asoc.2010.07.002Google Scholar
- 11.Sedano, J., Curiel, L., Corchado, E., Cal, E., Villar, J.R.: A soft computing method for detecting lifetime building thermal insulation failures. Integrated Computer-Aided Engineering 17(2), 103–115 (2009)Google Scholar
- 12.Corchado, E., Arroyo, A., Tricio, V.: Soft computing models to identify typical me-teorological days. Logic Journal of thel IGPL (2010), doi:10.1093/jigpal/jzq035Google Scholar
- 13.Michael, T., Wicker, B., Wicker, L.: Handbook of applied Multivariate Statistics and Mathematical Modeling. In: Discriminant Analysis. Academic Press, London (2000)Google Scholar
- 15.Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representation by error propagation. In: Parallel distributed processing: explorations in the microstructures of cognition, vol. I. MIT Press, Cambridge (1986)Google Scholar
- 19.Jolliffe, I.T.: Principal Component Analysis. Springer, New York (1986)Google Scholar