Physical Approach to the Automated Ultrasonic Characterisation of Buried Weld Defects in Ferritic Steel

摘要

The problem of automatically discriminating both smooth and rough cracks from more benign volumetric flaws such as porosity and slag, using pulse-echo ultrasound is discussed. Unlike many previous approaches, digital ultrasonic data were collected from tranducers scanned over the whole of each reflector. Scans were also made using different angles of ultrasound. Qualitative physical models for the interaction of ultrasound with these defects are developed to identify three independent effects that, together, could be used to distinguish between these four classes of defect. Each effect is quantified by numerical features computed from the ultrasonic data and criteria are developed to select one feature for each effect. Automated defect classification is then achieved by a weighted minimum distance pattern recognition algorithm. The preliminary application of this approach to a database containing feature values from 40 buried defects in ferritic steel welds gave a classification success rate of 100 percent.