| ABSTRACT: | AUTOMATIC CLASSIFICATION OF DEFECTS IN TIME-OF-FLIGHT-DIFFRACTION
O. Zahran, W. Al-Nuaimy
Department of Electrical Engineering & Electronics, The University of Liverpool, Brownlow Hill,
Liverpool, United Kingdom
Ultrasonic Time-Of-Flight Diffraction (TOFD) is a recent innovation that has proved highly effective for the
inspection of steel plates and tubular pipelines and has started to take its way to replace the other ultrasonic
testing techniques.
TOFD technique has a lot of advantages which make it the preferable technique in material testing. This
technique gives accurate sizing, positioning and characterising of weld and other defects with a high
probability of detection.
It is anticipated that coupled with the necessary processing algorithms, TOFD can be used for a
comprehensive automatic inspection of the weld areas with satisfactory levels of accuracy and reliability.
Currently most of the TOFD data interpretation is done manually, requiring operator skill, experience and
most significantly time. In light of the industrial pressure, the recent trend is to partially or fully automate
the inspection and data interpretation process, which could potentially improve the inspection and
interpretation procedures by adding and element of robustness and consistency by utilising computational
tools that are better suited to discriminating between subtle variations in visual and spectral properties of the
data; furthermore, save money, effort and time.
Advanced signal and image processing techniques have been developed to characterise TOFD signals and
extract distinguishable features to be used in defect detection and classification. Several features have been
investigated and selected, which prove to produce a good discrimination between defect and non-defect and
also between different defect types.
Combining these features with a multi-layer perception artificial neural network allows the TOFD images
segmentation and classification of detected defects in a fully automatic and un-supervised manner.
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