# Imaging Microstructure on Optically Rough Surfaces Using Spatially Resolved Acoustic Spectroscopy
## 3 Results
### Roughness Comparators
The overall quality of the measurement decreases with increasing roughness as more dropouts are seen, and the shift in mean velocity is apparent for the roughest samples. Figure 4: Results from grit blast comparator demonstrating the key features of laser ultrasound measurements on rough surfaces. (**a**) The light returned to the detector (blue) and percentage of positions on the sample where no measurement could be made (red). (**b**) Velocity standard deviation with averaging. (**c**) Mean velocity (bold) with increasing roughness, where the filled region represents velocity standard deviation. (**d**) SRAS velocity maps overlaid onto the grit blast comparator from which they were captured. It would be desirable to have a direct relationship between surface roughness and the quality of the ultrasonic measurement. Figure 5 shows that the roughness does not correlate with the SNR obtained. It is easy to demonstrate why this is the case by examining the reflected beams from surfaces of the same notional roughness. Figure 6a-d show an image of the reflected beam from four samples that all have an \(R_{a}\) of 0.8 \(\upmu\)m. It can clearly be seen that machining methods in particular can impart a strong directionality onto the reflection. Tabulated results for the comparators are given in Tables 3 and 4, reporting the average single-shot SNR when there is sufficient light returned from a point on the surface and the percentage of lost data points for each sample. These tables present the difficulty of working on different surface types and degrees of roughness. The SNR table explains how good data are likely to be from this surface if sufficient light is returned, and the dropout table explains how good an image of the surface is likely to be. In general, if light is returned to the detector, then the single-shot SNR is good from \(\sim\)11 to 27 dB. For most surfaces, the SNR is similar across the range of roughness levels present. In some cases, the roughest surface actually produces higher SNR data, for example, in grinding, which is likely due to the increase in the correlation length for the roughest sample; the feature sizes look significantly coarser to the eye, reducing the impact of the roughness on the ultrasound propagation. Figure 5: Surface roughness versus SNR; the \(R_{a}\) does not generally correlate with the SNR obtained. Figure 6: Comparison of reflections from four specimens with a surface roughness of 0.8 \(\upmu\)m. Despite having the same \(R_{a}\) value, there is a significant difference between the reflections. In (**a**) horizontal milling, (**b**) vertical milling, and (**c**) honing, highly directional responses are seen, with the light following the direction of the machining tool. In contrast, a nearly Gaussian reflection is seen from (**d**) grit blasting. Two samples show a marked drop in SNR with increasing roughness (grit blasting and spark erosion). These surface preparations show that as the roughness increases, as does the feature size (or correlation length); this is essentially starting as a surface where small-scale roughness dominates and then becomes a surface where waviness dominates, eventually producing features with such large scale they can be considered the form. Roughness leads to some scattering of the light, which, coupled with large surface angles for wavy features, leads to even more light being reflected outside of the collection optics, leading to a significant drop in light return. For two samples, honing and turning, the surfaces have very pronounced curvature, so there is a large change in surface height across the sample. This leads to a large number of lost points when the surface goes out of focus. When the surface is in focus, the data have generally good SNR. These types of surfaces would benefit from being able to ensure the sample is at the focal plane and normal to the surface. This requirement is discussed further in Section 4.1.