Characterizing Surface Quality: Why Average Roughness (Ra) Is Not Enough
Unlock the Potential of Surface Finish: How 3D Parameters Improve Insight and Performance
This application note explores the use of 3D parameters to characterize surface topography, as well as how 3D measurement can now provide a more complete picture of a surface's real variations and functional nature than was previously possible using Ra alone. Bruker experts explain the value and uses of different R and S parameters, then present two exclusive, real-world case studies in which these parameters were used for the design and development of high-performance surfaces.
Contents include:
- Figures and tables clarifying and comparing the value and potential uses of different R and S parameters.
- Two case studies showing the advantages of using S parameters for surface evaluation and design.
- A collection of relevant standards with definitions and guidelines for surface parameters.
KEYWORDS: 3D Optical Profiler; 3D Surface Metrology; Average Roughness; Surface Topography; R Parameters; S Parameters
Quantification of surface finish is both complex and necessary. Despite surface topography being three-dimensional, the most established surface measurement parameter is average roughness (Ra), a two-dimensional parameter. Ra is easy to measure and can be compared with historical data, but does nothing to describe a surface’s nuances or potential functionality. This application note explores the impacts of using 3D parameters to provide greater insight into surface finish and performance, including two case studies where the use of 3D parameters guided the design and development of high-performance surfaces.
Evolution from R Parameters to S Parameters
Stylus-based techniques for measuring surface finish were developed in the 1930s. For stylus measurements, a sharp or rounded tip is traced along the surface with its vertical deflection correlating to sample heights. Data from stylus measurements was quantified using R parameters, 2D descriptors including Ra, Rp (maximum peak height), Rv (maximum valley depth), Rt (total height), Rq (root-mean-square roughness), Rz (average of maximum peaks and minimum valleys), and others.
In the late 1900s, the 3D surface measurement technique optical profiling was developed and refined, enabling fast, large-area topographical analysis via areal data collection and multiframe stitching. These 3D datasets revealed much more about surface texture than a 2D trace, and Ra became an insufficient quantitative descriptor. Initially, Ra was just modified to be the equivalent in 3D, average surface roughness (Sa), but this neglected the rich information regarding surface height variation details and texture specifics that is stored in 3D datasets.
The S parameters (Table 1) were devised in the 1990s, and are grouped into four initial general categories: amplitude, spatial, hybrid, and functional. These parameters describe a surface more completely than 2D parameters, painting a quantitative picture of waviness, microroughness, wearability, lubricant retention capability, texture direction, and much more. Using the S parameters, engineers and process designers can understand their surfaces in greater detail and can design surfaces with a focus on functionality.
Another way to understand and represent surface texture is the bearing area curve (BAC, also known as a bearing ratio curve or an Abbott-Firestone curve). The BAC, shown in Figure 2, is the cumulative probability density function of height for a surface profile line. That profile can come from either a single trace (for 2D data) or an average over multiple traces (for 3D data). This Abbott curve is also used for the evolution of the 3D volume parameters for fluid characterization, such as Sci (core fluid retention index) and Svi (valley retention index).
Persistence and Weaknesses of Ra
Surface finish is still often described using only an Ra value, despite its inability to capture the nuance of real surfaces. This lingering attachment to Ra is due to two main factors: ease of low-cost 2D measurements with a stylus profilometer, and the existence of historical data for Ra. While it does remain useful as a general surface texture guideline, Ra is too general to describe a surface’s real variations or functional nature.
A surface with sharp spikes and deep pits or one with general isotropy may yield the same Ra value. Figure 3 shows four surfaces with the same Ra for different finishing steps, producing visually distinct surfaces that would functionally perform very differently from each other. Ra calculated from a single trace (or even several) cannot distinguish these surfaces and cannot provide information about their functionality, while S parameters can do both.
The surfaces from Figure 3 were evaluated using (1) several stylus-collected Ra measurements, and (2) Bruker’s white-light interferometry (WLI) to calculate S parameters and perform a stylus analysis that correlates back to stylus measurements. Ra and S-parameter results are plotted in Figure 4. With guidelines connecting the same parameter across samples on the plot, it is clear that the advertised and included certified values of Ra are very close for all four fingernail standard samples (nearly horizontal lines at Ra = 400 µm). All other measurements deviate from these provided values of Ra, though. An independent certification and WLI stylus analysis–calculated Ra values (which are based on an average over an area) show great correlation with each other, only deviating for the vertical milling sample where the stylus measurement location was unknown. For more information on these measurements and how Bruker’s Vision64® software can facilitate stylus analysis of WLI areal data, refer to Bruker Application Note 558, “Correlating Advanced 3D Optical Profiling Surface Measurements to Traceable Standards”.
The two plotted S parameters from WLI measurements (summit density Sds and structure angle Std) in Figure 4 show different distinctions between the samples than Ra does. In particular, vertical milling had a much higher Std (due to the angle of the dominant surface structure) and lower Sds (due to the lower summits per unit area) than the other three finishing steps. It is obvious that single- or multiple-trace Ra does not provide a complete picture of the differences between these surfaces. Averaging Ra over a larger area begins to clarify variations, and adding an analysis of S parameters furthers understanding both of differences and of what those differences could functionally mean.
Case Study 1: Determining a Source of Corrosion
Ra is not necessarily an effective quality screen or an adequate measure for development and problem solving. At Masco Corporation, Research & Development, incoming ASTM 366 coil steel stock was conforming to an average roughness specification of 20 to 70 microinches, but a significant portion of the stock had corroded after a series of cold working and surface treatment processes.
To determine the source of the rust, surface analysis was performed on the incoming stock. Figure 5 shows 3D optical profiler plots of the different stock surfaces that resulted in either acceptable or rust-prone final parts. Many deep valleys can be seen on the rust-prone stock, whereas the acceptable stock is more isotropic. Of the S parameters, skewness (Ssk) and valley depth (Sv) were found to correlate well with the tendency towards corrosion.
In Figure 6, a BAC was plotted for both types of stock, indicating the percentage of the surface that falls above or below a certain depth. These curves quantified the percentage of valley area that tended to lead to corrosion. From this data, it was determined that the deeper valley structure tended to hold processing solutions and did not rinse or dry properly, allowing flash rusting to occur. A ratio of parameters derived from the bearing area analysis was an excellent indicator of the incoming stock’s tendency to corrode.
Case Study 2: Using 3D Parameters to Engineer a Surface
The engineering of a surface for a new part requires more than just an Ra value. A new clutch plate design at Steel Parts needed to have the best friction and wear performance. After several plate designs with known performance characteristics had been evaluated (Figure 7), it was determined that skewness and kurtosis correlated well with wear and friction, as did several other combinatorial parameters. These parameters were used to successfully develop and control a novel manufacturing process that ensured consistent part performance.
Conclusions
Advances in 3D measurement techniques such as optical profiling have given engineers, process designers, and quality control professionals a significantly improved toolkit for describing surfaces. 3D parameters uniquely differentiate not only surface shape but functionality as well. A careful surface design study results in a better understanding of functional characteristics, a more controllable process and, ultimately, better surface performance. For more definitions and usage guidelines for surface parameters, see the standards listed in Table 2.
Authors
- Roger Posusta, Senior Marketing Application Specialist, Bruker (roger.posusta@bruker.com)
- Sandra Bergmann, Product Line Manager, Bruker (sandra.bergmann@bruker.com)
- Erica Erickson, Ph.D., Materials Science Writer, Bruker (erica.erickson@bruker.com)
©2024 Bruker Corporation. Vision64 is a trademark of Bruker Corporation. All other trademarks are the property of their respective companies. All rights reserved. AN582, Rev. A0.
