A physical approach to the automated ultrasonic characterization of buried weld defects in ferritic steel

• Published in: NDT international Vol. 19; no. 3; pp. 145 - 153
• Main Authors: Burch, S.F., Bealing, N.K.
• Format: Journal Article
• Language: English
• Published: Guildford Elsevier B.V 01.06.1986; Butterworth Scientific Limited
• Subjects: Analysing. Testing. Standards; Applied sciences; Buildings. Public works; crack characterization; Cross-disciplinary physics: materials science; rheology; Exact sciences and technology; ferritic steels; Industrial metrology. Testing; Joining, thermal cutting: metallurgical aspects; Materials science; Mechanical engineering. Machine design; Metals, semimetals and alloys; Metals. Metallurgy; Non-destructive testing: methods and equipments; Nondestructive testing; Physics; pulse-echo ultrasound; Specific materials; Structural analysis. Stresses; Testing for defects; weld defects; Welding; weld defects; pulse-echo ultrasound; ferritic steels; crack characterization; Automation; Ferritic steel; Ultrasonic testing; Weld defect; Ultrasonic control; Defect characterization; Nondestructive test; Welded joint; Signal processing; Digital processing; Weld
• ISSN: 0308-9126
• DOI: 10.1016/0308-9126(86)90103-3

This paper is concerned with the problem of automatically discriminating both smooth and rough cracks from more benign volumetric flaws such as porosity and slag, using pulse-echo ultrasound. Unlike many previous approaches, digital ultrasonic data were collected from transducers 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%.