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| Reconstruction of Stress Corrosion Crack Using Neural Network and Support Vector Machine Techniques |
| Geng Qiang, Tian Shuxia, Huang Taihui, Chen Zhenmao |
| MOE Key Laboratory for Strength and Vibration Xi’an Jiaotong University Xi’an 710049 China |
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Abstract Stress corrosion crack (SCC) has a complex micro-structure similar with bifurcated tree branches and its non-destructive quantitative evaluation is difficult. In this paper, reconstruction schemes based on artificial neural networks (ANN) and support vector machine (SVM) are proposed based on a conductive crack model. ECT signals from a pancake coil probe scanned just over the crack are taken as the source signals for the crack reconstruction. A lot of sample ECT signals are generated using a FEM-BEM hybrid code as data sets for ANN and SVM training. Special ways for crack parameterization and signal feature extraction are introduced to improve the reconstruction precision. The numerical results show that the SVM method has a better performance than the neural network approach in the reconstruction of SCC for noise polluted signals.
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Received: 09 December 2009
Published: 04 March 2014
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2010, Vol. 25 
