Advances in real time radiography, or 'radioscopy', are serving to reduce the limits on the speed, flexibility and performance of inspection cycles. The counter result of these advances is that conventional manual inspection can often no longer cope with the inspection rates and intensities required. To address this problem various work has been directed at automating the inspection task by various researchers worldwide. The most difficult problem in the inspection cycle is the accurate detection of defects in a given radioscopic image, and it is to this area which the majority of research has been applied, though with limited success. The general failings of the majority of published techniques can be attributed to four key areas:(1) unacceptable false alarm rates due to component structure and noise, (2) inability to detect defects of all orientations and types, (3) inability to detect defects across different applications, and (4) non realistic computation times.
For these reasons, work in fully automated on-line radiographic inspection has been investigated by the MSRR group for a number of industrial NDT applications. The bulk of the research activity in this area is applied firstly on image segmentation for defect and flaw detection. The methods developed for this task include artificial neural networks (ANN's)(see Fig.(1)), grey level morphology, adaptive threshholding (see Fig.(2)) and multi-channel filtering schemes. Once potential defective areas in an image have been highlighted by these operators, artificial intelligence techniques, including both expert systems and ANN's, have been successfully applied to provide full defect classification and overall product quality interpretation.
These pages are administered by Maarten Michielen
(m.michielen@surrey.ac.uk)
All legal boffs please read this copyright and disclaimer notice.
Last change: January 2000
