4 First attempts at an improved methodology for examination of surface texture changes
4.4 Surface texture measurements
4.4.3 Texture analysis using bespoke software
A commonly used, industry standard, texture analysis software package, MountainsMap by Digitalsurf was used to calculate a wider range of roughness, spacing and surface volume parameters using a number of similar surface filters. Some of the parameters calculated are shown in Table 4.2. Two types of filter were used: a surface levelling filter using subtraction from a fitted least square plane; and Gaussian low pass filters with upper wavelengths 0.025 mm, 0.08 mm, 0.25 mm and 0.8 mm.
0.0 0.2 0.4 0.6 0.8 1.0
0 2 4 6 8
Sa/ normalised to maximum value
Polishing level
Lowpass 0.08 mm Bandpass 0.25-0.5 mm Lowpass 0.25 mm Bandpass 0.5-0.8 mm Bandpass 0.08-0.25 mm
Table 4.2 Roughness and spatial parameters calculated using MountainsMap
Parameter Description
Sq Root mean square height of the surface
Ssk Skewness of height distribution
Sku Kurtosis of height distribution
Sp Maximum height of peaks
Sv Maximum height of valleys
Sz Maximum height of the surface
Sa Arithmetical mean height of the surface
Spd Density of peaks – number of local maxima per areas
The graph in Figure 4.25 shows average values of Sa measured at each polishing level for all the filters applied, normalised to the maximum value of Sa. As observed in the previous sections, there are no clear trends for changes in Sa with increased polishing, regardless of the filter applied. The graph in Figure 4.26 shows normalised values for all calculated parameters measured on the software levelled surfaces. The pattern of changing roughness with polishing is similar for all but the skewness measure (Ssk), and to a lesser extent, the kurtosis measure (Sku). However, the difference in the apparent trend is largely attributable to an outlying average value at polishing level 7.
Figure 4.25 Average normalised Sa for each polishing level after application of various filters
Figure 4.26 Average normalised roughness parameters for each polishing level on software levelled surfaces
0 0.2 0.4 0.6 0.8 1
0 2 4 6 8
Sa/ normalised to maximum value
Polishing level
Original surface Gaussian 0.8mm Levelled Surface Gaussian 0.025mm Gaussian 0.25mm Gaussian 0.08mm
0 0.2 0.4 0.6 0.8 1
0 2 4 6 8
Roughness / normalised to maximum value
Polishing level
Sq Levelled Surface Sz Levelled Surface Sa Levelled Surface Ssk Levelled Surface Sp Levelled Surface Sku Levelled Surface Sv Levelled Surface
4.5 Discussion
Analysis of high resolution photographs taken of aggregate surfaces as they were polished in the laboratory demonstrated that changes in pixel intensities could be linked to the amount of polishing applied. However, the analysis was only successful when areas of the surface that had not been replicated were considered. If the effect of replication is simply to clean the aggregate surface then the implication is that image analysis only works because texture is highlighted by contrasting detritus trapped within the aggregate texture. Although in-service roads are not likely to be clean, this phenomenon could present issues for calibration and consistency when using images of the road in a practical system. This experiment has not allowed further investigation of the link between visual and physical changes because surface texture measurements (and SEM analyses) were carried out on replicas of the surface in areas that presented no obvious visual change. Consequently, although this part of the experiment supports the potential for use of image analysis in the long-term it has not provided information about polishing mechanisms and the differences between aggregate types.
Scanning electron microscopy was used to verify the fidelity of replication and then to compare identical areas from the replica of the before-polishing surface with the replica of the after-polishing surface to observe changes in the surface texture as it is polished. This was achieved by using prominent features of the surface (such as stone edges or distinctive ridges) for navigation so the exact location could be compared (either stone to replica or before- to after-polishing). Although the resolution of replication was not as high as the manufacturer’s claim, features as small as 5 µm were easily identifiable. Qualitative observations of changes to surface texture before and after polishing are the most compelling outcome from this work even though the intention was simply to illustrate the sorts of texture change that would subsequently be measured using three dimensional focus variation microscopy.
Quantitative measurements of surface texture were made on the replica plaques by scanning a large number of discrete areas with a focus variation measurement microscope. Standard roughness parameters, calculated without prior filtering, did not demonstrate a change in surface roughness due to polishing. Various filters were applied to the surface in an attempt
to discount the effect of surface form but no trends in the data became apparent.
The methodology assumed that a sufficiently large number of measurements would allow characterisation of the surface as a whole.
Although an approximate record of the location of each measured area was kept, no attempt was made to undertake surface texture measurements on the specific areas that had been viewed by SEM. It is difficult to verify that the areas measured were replicated from parts of the aggregate surface that came into contact with the W-S machine’s polishing rollers. It was assumed that some trend would be immediately apparent in any measure of surface roughness without the need to first identify suitable parameters based on observed changes. Furthermore, the type of aggregate used may polish in a more complicated way than can be easily characterised using simple roughness parameters.
The replication technique was convenient, allowing experimental work to be carried out in several laboratories with a minimum amount of travel and the easy use of SEM (the aggregate specimen being too big to fit in the microscope chamber), but its use adds a potential source of error, even though its fidelity was demonstrated. So, instead of carrying out any further analysis with the data from this experiment it seemed more sensible to refine the data collection methodology. If qualitative and quantitative analysis of the same areas, throughout the polishing process, can be achieved then identification of appropriate characterisation parameters might be made easier. Furthermore, if it can be verified that the polishing rollers in the W-S machine actually come into contact with the surfaces measured then there can be more confidence in the dataset.
The next chapter describes in detail a new, refined, methodology. It uses aggregates with simpler mineralogy, surface navigation techniques developed for inspection by SEM above, measurements made on aggregate surfaces rather than on replicas and a paint removal technique that identifies the areas of aggregate surface that come into contact with the W-S machine’s polishing rollers.
4.6 References
British Standards. (1998). BS EN ISO 4288. Geometric Product
Specification (GPS) - Surface texture - Profile method: Rules and procedures for the assessment of surface texture. London: BSi.
British Standards. (2008). BS ISO 25178-2. Geometric Product Specification (GPS) - Surface texture: Areal. Part 2: Terms, definitions and surface texture parameters. London: BSi.
Dunford, A. (2010). PPR538. Measuring skid resistance without contact;
2009-2010 progress report. Crowthorne: TRL.
James, J. G. (1967). Calcined Bauxite and other atificial, polish resistant, roadstones. Road Research Laboratory.
Thompson, A., Burrows, A., Flavin, D., & Walsh, I. (2004). The Sustainable Use of High Specification Aggregates for Skid Resistant Road
Surfacing in England. East Grinstead: Capita Symonds Ltd.
West, G., & Sibbick, R. G. (1988). Petrographical comparison of old and new control stones for the accelerated polishing test. Quarterly Journal of Engineering Geology, 21, 375-378.