Fusion of geometric and thermographic data for automated defect detection

Beata Oswald-Tranta; Paul L. O'Leary

doi:10.1117/1.JEI.21.2.021108

7 May 2012 Fusion of geometric and thermographic data for automated defect detection

Beata Oswald-Tranta, Paul L. O'Leary

Author Affiliations +

Journal of Electronic Imaging, Vol. 21, Issue 2, 021108 (May 2012). https://doi.org/10.1117/1.JEI.21.2.021108

Abstract

Many workpieces produced in large numbers with a large variety of sizes and geometries, e.g. castings and forgings, have to be 100% inspected. In addition to geometric tolerances, material defects, e.g. surface cracks, also have to be detected. We present a fully automated nondestructive testing technique for both types of defects. The workpiece is subject to continuous motion, and during this motion two measurements are performed. In the first step, after applying a short inductive heating, a thermographic measurement is carried out. An infrared camera records the surface temperature of the workpiece enabling the localization of material defects and surface cracks. In the second step, a light sectioning measurement is performed to measure the three-dimensional geometry of the piece. With the help of feature-based registration the data from the two different sources are fused and evaluated together. The advantage of this technique is that a more reliable decision can be made about the nature of the failures and their possible causes. The same registration technique also can be used for the comparison of different pieces and therefore to localize different failure types, via comparison with a "golden," defect-free piece. The registration technique can be applied to any part that has unique geometric features, around which moments can be computed. Consequently, the inspection technique can be applied to many different parts. The efficacy of the method is demonstrated with measurements on three parts having different geometries.

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Beata Oswald-Tranta and Paul L. O'Leary "Fusion of geometric and thermographic data for automated defect detection," Journal of Electronic Imaging 21(2), 021108 (7 May 2012). https://doi.org/10.1117/1.JEI.21.2.021108

Published: 7 May 2012

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