Disbond detection through ultrasonic signal classification using an artificial neural network

An evaluation of artificial neural networks applied to infrared thermography inspection of composite aerospace structures

“…Neural networks are known for their high processing speed, high classification accuracy, low sensitivity to noise,and easy thresholding capability to yield a binary image for automated detection. Recently they have gained a lot of attention and have been investigated in conjonction with several NDT techniques, the training being done either with experimental ( real or simulated defects), or with theoretical data [3][4]. However results obtained with training on experimental data do not seem as encouraging as with those obtained with theoretical one's for two major reasons, firstly a large training set is required which is difficult to obtain with real cases, secondly simulated cases are not representative enough.…”

A novel approach to the classification of ultrasonic NDE signals

“…He shows how both parametric and nonparametric discriminant analy sis can be applied to determine how well the quantitative analysis compares with the qualitative diagnosis supplied for each case. Prabhu [7] uses an ultrasound technique for detecting disbonds in aircraft lab joints and in the adhesive joints between aircraft skin and reinforcing doublers. He uses an artificial neural network for classifying signals corresponding to bonded and disbonded regions.…”