Special Section on Quality Control by Artificial Vision

Thermographic crack detection and failure classification

[+] Author Affiliations
Beata Oswald-Tranta

University of Leoben, Institute for Automation, Peter-Tunnerstraße 27, A-8700 Leoben, Austria

Mario Sorger

University of Leoben, Institute for Automation, Peter-Tunnerstraße 27, A-8700 Leoben, Austria

Paul O’Leary

University of Leoben, Institute for Automation, Peter-Tunnerstraße 27, A-8700 Leoben, Austria

J. Electron. Imaging. 19(3), 031204 (July 08, 2010). doi:10.1117/1.3455991
History: Received August 03, 2009; Revised January 18, 2010; Accepted January 25, 2010; Published July 08, 2010; Online July 08, 2010
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In the case of thermographic inspection, the workpiece is heated in a particular manner followed by the observation of the resulting temperature increase at the material surface by means of an infrared camera. Inhomogeneities such as surface cracks cause a nonuniform distribution of the temperature; consequently, they can be localized in the infrared images. For metallic pieces, the most efficient way is inductive heating, whereby the induced eddy current generates heat directly in the surface skin of the sample. Experiments have been carried out on how steel workpieces, especially castings, can be thermographically inspected to detect cracks. The testing is a nondestructive and contact-free method. The goal is to develop a fully automated testing equipment with high throughput, where the flawed pieces are identified by evaluation and classification of the infrared images. The classification task is to distinguish between temperature increase around a crack and additional heating at the edges of the workpieces. Neural network has been used to train and to classify about 750 images, and good results have been achieved.

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Citation

Beata Oswald-Tranta ; Mario Sorger and Paul O’Leary
"Thermographic crack detection and failure classification", J. Electron. Imaging. 19(3), 031204 (July 08, 2010). ; http://dx.doi.org/10.1117/1.3455991


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