Automatic Defect Characterisation in Ultrasonic NDT

T. Stepinski and F. Lingvall

15th World Conference on NDT, Rome, Italy, 15-21 October, 2000

Abstract:

Paper reports results of the project concerned with development of tools for automatic defect characterisation based on ultrasonic data. The goal of the project was to develop a software tool facilitating interpretation of flaw signatures obtained from pulse-echo ultrasonic measurements. A neural network (MLP) processing outputs from a specialised feature detectors was used for the characterisation. Different ways of feature extraction and flaw position estimation are presented and discussed. In the experimental part, measurements on artificial flaws (notches and holes) are shortly reported first, and then, the results obtained for ultrasonic measurements performed on welded carbon steel blocks including 36 different "natural" defects implanted into V-welds are presented in more detail. The flaw population was divided in two major groups, sharp flaws (various types of cracks and lack of fusion) and soft types of flaws (slag, porosity and over-penetration). A large amount of B- and D-scan data was acquired using 6 different angle transducers. The evaluation of these measurements resulted in the conclusion that the signal variation within a certain class of defects was considerably larger than the corresponding variation found in signals from artificial and simulated defects. Steel block measurements also revealed that some of the defect types were hard to distinguish, particularly if only traditional features, like, fall/raise times, pulse duration and echo dynamics were used. To overcome this difficulty more powerful feature extraction methods were employed, such as, discrete wavelet transform and principal component analysis. In conclusion, difficulties encountered in automatic flaw classification are discussed and possible solutions are presented.