This thesis documents the use of the Total Focusing Method (TFM) for performing ultrasonic C-scans of components with irregular and curved surfaces. Ultrasonic inspection depends on an understanding of the link between an ultrasonic indication and flaw size. Some existing methods at Rolls-Royce use target defects of a known size and metal path through flat surfaces to create a Distance Amplitude Curve (DAC) that can be used to accurately size flaws. This study investigated a DAC equivalent for surface-adaptive TFM techniques applied through non-planar surfaces. There was business interest in development of this technique, and also in its delivery by use of general purpose robotic manipulators. Firstly, a robotic inspection process was developed, and a C-scan was conducted on an irregular titanium plate. cueART, software developed by the University of Strathclyde was adapted to create a TFM C-scan. Results were found to be comparable to an existing production inspection process. A series of curved test pieces were manufactured and target Flat-Bottomed Hole (FBH) defects were added. BRAIN, software developed by Bristol university, was adapted and used with equipment at Rolls-Royce to conduct TFM C-scans. DAC curves were generated experimentally for a test inspection, showing convex radii from 10 to 40 mm led to a drop of 2 dB, and concave 10 dB. This was repeated at various depths to produce multiple DAC curves. Modelling techniques for these experimental cases were developed, finding that a 2D finite element model, and 3D CIVA model followed a similar trend to the DAC. Modelling was used to extend results to produce a map of peak flaw signal through a curved surface. An experimental study of flat, rough surfaces showed that an Ra of 12 μm led to a 6.4 dB reduction in peak TFM signal, and13.2 dB reduction in signal to noise ratio.
| Date of Award | 23 Nov 2019 |
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| Original language | English |
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| Awarding Institution | - University Of Strathclyde
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| Sponsors | University of Strathclyde & EPSRC (Engineering and Physical Sciences Research Council) |
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| Supervisor | Gareth Pierce (Supervisor) & Anthony Gachagan (Supervisor) |
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