Automatic detection of defects in ultrasonic testing using artificial neural network Online publication date: Wed, 19-Jan-2011
by S. Sambath, P. Nagaraj, N. Selvakumar, S. Arunachalam, T. Page
International Journal of Microstructure and Materials Properties (IJMMP), Vol. 5, No. 6, 2010
Abstract: An artificial neural network with signal processing technique is proposed to improve the sensibility of flaw detection and to classify defects in ultrasonic testing. Features for discrimination of detected echoes are extracted in discrete wavelet representation and are then classified using ANN. The inputs of the ANN are the features extracted from each ultrasonic oscillogram. Two different types of defect are initially considered namely crack and porosity. The training of the ANN uses supervised learning mechanism and therefore each input has the respective desired output. The available dataset is randomly split into a training subset (to update the weight values) and a validation subset. With the wavelet features and ANN, good classification at the rate of 96% is obtained. According to the results, the algorithms developed and applied to ultrasonic signals are highly reliable and precise for online quality monitoring.
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