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Index-based triangulation method for efficient generation of large three-dimensional ultrasonic C-scans

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The demand for high-speed ultrasonic scanning of large and complex components is driven by a desire to reduce production bottlenecks during the non-destructive evaluation (NDE) of critical parts. Emerging systems (including robotic inspection) allow for the collection of large volumes of data in short time spans, compared to existing inspection systems. To maximise throughput, it is crucial that the reconstructed inspection datasets are generated and evaluated rapidly without loss of detail. This requires new data visualisation and analysis tools capable of mapping complex geometries while guaranteeing full coverage. This paper presents an entirely new approach for the visualisation of three-dimensional (3D) ultrasonic C-scans, suitable for application to high data throughput ultrasonic phased array inspection of large and complex parts. Existing reconstruction approaches are discussed and compared with the new index-based triangulation (IBT) method presented. The IBT method produces 3D C-scan representation, presented as coloured tessellated surfaces, and the approach is shown to work efficiently, even on challenging geometries. An additional differentiating characteristic of the IBT method is that it allows for easy detection of lack of coverage (an essential feature for ensuring that inspection coverage can be guaranteed on critical components). The results demonstrate that the IBT C-scan generation approach runs over 60 times faster than a C-scan display based on Delaunay triangulation and over 500 times faster than surface reconstruction C-scans. In summary, the main benefits of the new IBT technique include: high-speed generation of C-scans on large ultrasonic datasets (orders of magnitude improvement compared to surface reconstruction C-scans); the ability to operate efficiently on 3D mapped datasets (allowing 3D interpretation of C-scans on complex geometry components); and intrinsic indication of lack of inspection coverage.
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Document Type: Research Article

Publication date: April 1, 2018

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