## 6 Experimental Results
Imaged within RoboDK. (**a**) Path, and in high-frequency segments the laser-discovered positions on the part. (**b**) Original Point cloud of the part and environment. Figure 6: Point cloud and C-scan of the part. The average error of RGB/D point cloud poses from the best fit plane of the collected laser data is 14.7 mm. (**a**) Point cloud map of a reflective CFRP component. Some sections of the point-cloud are missing, the surface data is considerably irregular. Visual artefacts incorrectly placed within the part are circled. (**b**) Heat-mapped C-scan of the part. The 5 mm thickness of the CFRP component placed the back-wall signal within the dead-zone of the 5 MHz probe. The support strut of the part can be seen within the gated region as higher-heat. C-scan representations of the data have been validated by imaging the calibration block, seen in Figure 8. The three side-drilled holes of varying depth are clearly seen on the coloured digital-twin of the surface, highlighted within the image. Both a smooth and a rough CFRP sample were used to test friction data at variable pressures and speeds. The results are presented as histogram values in Figure 9.  
Figure 8: The three ascending side-drilled holes can be seen in the solid box. One disadvantage of this path planning method is the potential for the tool to go over the lip of the part, presenting a second reflection within the gated region as seen in the dashed box. This can be gated out by an operator, as shown. (**a**) Initial image of the part, with static gating applied. (**b**) After dynamic gating, the three side-drilled holes are highlighted, surface reflections caused by non-planar probe apertures have been removed. Figure 7: Structural-similarity based part localisation and path generation process for a calibration block with side-drilled holes. (**a**) Calibration image showing just the workcell. (**b**) Subsequent image with the part present. (**c**) Deducted image—kept pixels are shown in white, discarded pixels in black. (**d**) Resulting point cloud after remaining point clustering and selection. The identified part is in blue, extracted way-points in red. While the measured friction across the surface can differentiate between the differing surface roughness values, there is a significant change in the rough surface's measured friction value. Changes in the hydrodynamic friction of the UT couplant with TCP speed, combined with a mechanical locking of the spring-mechanism explain the shift in friction values across the observed data. The mechanical design of the tool allows for an angled locking of the UT's sprung support-struts due to the force acting along the strut's central axis. For rougher surfaces, the frictional force in this direction is great enough to lodge the probe and then on the return, dislodges the tool. The angled face of the tool then also creates a 'bow-wave' of UT couplant in one direction, allowing the tool to more smoothly pass over the surface with increased lubrication. The result is a split in the frequencies of observed friction values for plates of un-varying roughness, with principle modes becoming visible with greater speed as hydrodynamic friction increases. This is particularly noticeable on the smooth sample, as an increase in speed thereby furthers the split in friction values. Both samples see the peaks of friction inversely correlated with tool load. On the Stribeck curve seen in Figure 1, the majority of the tool's aperture is in the well-coupled region, ensuring high quality UT data as there is a thick layer of couplant between the tool and part. This alternating bow-wave effect can also be seen in Figure 5.0  
At the raster end-points, the misalignment force is seen as spikes in the friction. The result is that along each raster-line, changes in the friction data can be accurately gathered. However, these data are not transferable across arbitrary raster line-paths.