Neural networks for defect detection in non-destructive evaluation by sonic signals

This paper presents an application of neural networks in pattern recognition of defects in sonic signals from non-destructive evaluation by multichannel impact-echo. The problem approached consists in allocating parallelepiped-shape materials in four levels of classifications defining material condition (homogeneous or defective), kind of defects (holes and cracks), defect orientation, and defect dimension. Various signal features as centroid frequency, attenuation and amplitude of the principal frequency are estimated per channel and processed by PCA and feature selection methods to reduce dimensionality. Results for simulations and experiments applying Radial Basis Function, Multilayer Perceptron and Linear Vector Quantization neural networks are presented. Neural networks obtain good performance in classifying several 3D finite element models and specimens of aluminum alloy.