A Constructive Neural Network to Predict Pitting Corrosion Status of Stainless Steel

The main consequences of corrosion are the costs derived from both the maintenance tasks as from the public safety protection. In this sense, artificial intelligence models are used to determine pitting corrosion behaviour of stainless steel. This work presents the C-MANTEC constructive neural network algorithm as an automatic system to determine the status pitting corrosion of that alloy. Several classification techniques are compared with our proposal: Linear Discriminant Analysis, k-Nearest Neighbor, Multilayer Perceptron, Support Vector Machines and Naive Bayes. The results obtained show the robustness and higher performance of the C-MANTEC algorithm in comparison to the other artificial intelligence models, corroborating the utility of the constructive neural networks paradigm in the modelling pitting corrosion problem.

[1]  Daniel Urda,et al.  Constructive Neural Networks to Predict Breast Cancer Outcome by Using Gene Expression Profiles , 2010, IEA/AIE.

[2]  Richard Simon,et al.  A comparison of bootstrap methods and an adjusted bootstrap approach for estimating the prediction error in microarray classification , 2007, Statistics in medicine.

[3]  Aboul Ella Hassanien,et al.  Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011, 6-8 April, 2011, Salamanca, Spain , 2011, SOCO.

[4]  Ignacio J. Turias,et al.  Austenitic Stainless Steel EN 1.4404 Corrosion Detection Using Classification Techniques , 2011, SOCO.

[5]  Mariano Marcos,et al.  Influence of chemical composition on the pitting corrosion resistance of non-standard low-Ni high-Mn–N duplex stainless steels , 2003 .

[6]  Ignacio J. Turias,et al.  Pitting corrosion behaviour of austenitic stainless steel using artificial intelligence techniques , 2012, J. Appl. Log..

[7]  Nick Birbilis,et al.  Modeling the environmental dependence of pit growth using neural network approaches , 2010 .

[8]  Leonardo Franco,et al.  Computational capabilities of feedforward neural networks: the role of the output function , 2007 .

[9]  Mathew J. Palakal,et al.  Computational simulation of multi-pit corrosion process in materials , 2008 .

[10]  Nicolás García-Pedrajas,et al.  Trends in Applied Intelligent Systems - 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Cordoba, Spain, June 1-4, 2010, Proceedings, Part I , 2010, IEA/AIE.

[11]  M. Kamrunnahar,et al.  Prediction of corrosion behaviour of Alloy 22 using neural network as a data mining tool , 2011 .

[12]  Leonardo Franco,et al.  A New Decomposition Algorithm for Threshold Synthesis and Generalization of Boolean Functions , 2008, IEEE Transactions on Circuits and Systems I: Regular Papers.

[13]  S. Lajevardi,et al.  Prediction of time to failure in stress corrosion cracking of 304 stainless steel in aqueous chloride solution by artificial neural network , 2009 .

[14]  Günter Schmitt,et al.  Global Needs for Knowledge Dissemination, Research, and Development in Materials Deterioration and Corrosion Control , 2009 .

[15]  Leonardo Franco,et al.  C-Mantec: A novel constructive neural network algorithm incorporating competition between neurons , 2012, Neural Networks.

[16]  Marcus R. Frean,et al.  A "Thermal" Perceptron Learning Rule , 1992, Neural Computation.