Optimization of the Cascade Feed Forward Back Propagation network for defect classification in ultrasonic images

Ultrasonic Time of Flight Diffraction (TOFD) is now a well established NDE technique finding wide applications in the industry for inspection during manufacture, pre-service and also inservice. While conventionally interpretations of UT images are done by the inspector, a need has always been felt for automated evaluation and interpretation especially when large inspection volumes are involved. Apart from enhancing the speed of inspection, automated evaluation and interpretation provides better reliability of inspection. A number of approaches based on signal analysis coupled with artificial neural networks (ANN) are being tried internationally and limited success has also been obtained. This paper focuses on the development of a semi automatic toolbox for reliable and fast flaw classification in TOFD images using ANN. TOFD images are first acquired and statistical parameters such as mean, standard deviation, energy, skewness and kurtosis are calculated for the region of interest in the images. The classification of the flawed region like Crack, Lack of Fusion, Lack of Penetration, Porosity and Slag Inclusion was materialized using different ANN approaches which made use of these statistical parameters as their input. The process of optimization of a network involves comparison of classification accuracy and the sensitivity of the selected networks. The Cascade Feed Forward Back Propagation (CFBP) network with log sigmoidal activation function proved to be the optimized network model for the data set considered in this study.