Classifying Ultrasonic Resonance Spectra Using Neural Network

TadeuszStepinski, Lars Ericsson, B. Vagnhammar, and Mats Gustafsson.

Materials Evaluation
vol 58, January 2000, pp. 74-79.

Abstract:

In this paper we present a neural network based spectrum classifier (NSC) and its application to ultrasonic resonance spectroscopy (URS). URS is a method for testing stiff materials by exciting the sample under test into mechanical resonance by progressively sweeping the frequency of a driving transducer across a certain frequency range. The use of an Artificial Neural Network (ANN) is used for spectrum classification to meet the requirements of high sensitivity for small but relevant changes in the spectra, and simultaneous robustness against measurement noise. Among several types of ANNs that could be used for classifying the spectra we have chosen a multi-layer perceptron (MLP). Although the MLP itself can perform feature extraction, we included an optional pre-processor for this purpose. The NSC is essentially model free and can be trained using real and modeled spectra. The classifier uses both amplitude and phase information in the spectra. The performance of the classifier has been verified using a number of practical applications, such as, aerospace composite structures, ball bearings and aircraft multi-layer structures. Here, we present in more detail results of NSC application to detection of disbonds in adhesively joint multi-layer aerospace structures using Fokker Bond Tester resonance instrument. In this case the classifier is capable of detecting very small disbonds (larger than 25% of the sensor area) and correct identifying their position in the structure (identifying the defected joint).