Predicting sealing quality by filtering surface texture data

Modern texture analysis software can predict sealing performance and address root causes of potential leakage.

Figure 1. By properly analyzing surface texture, leak paths on a sealing interface can be predicted.
Figure 1. By properly analyzing surface texture, leak paths on a sealing interface can be predicted.
All images courtesy of Digital Metrology Solutions

The shape and texture of a surface is critically important to its functionality. This is particularly true in sealing applications. Today, there is no shortage of instruments for measuring surface texture. Yet, despite the great amount of data that can be gleaned from surface texture measurements, most engineers continue to specify only basic height-based parameters, such as average roughness or total flatness. (See Figure 1.)

The challenge, however, is that most surface functionalities (sealing, for example) are not measurable purely in units of height. Local curvatures and the connectedness of surface features can provide much more valuable insight.

In recent years, morphological filters have been introduced in surface texture literature and standards. The morphological opening and closing filters provide a powerful means of simulating surfaces in contact. Furthermore, they are well-suited to predicting how a relatively soft surface (e.g., a gasket or seal) will interact with a relatively rigid surface. This article discusses how sealing performance can be predicted with this analysis via modern surface texture analysis software.

Can average roughness distinguish a “good” sealing surface?

As previously mentioned, the most commonly specified surface finish parameters in sealing applications are based on heights measured along a profile. These common parameters include average roughness (Ra) and average peak-to-valley height (Rz). In some cases, a waviness height (Wt) may also be specified. These parameters are well-known and readily measurable with most surface profiling instruments.

Unfortunately, these statistics do not necessarily indicate how the surface will perform. Figure 2 shows two very different surfaces with virtually identical Ra, Rz and Wt values. If these parameters were specified on a print, the two surfaces would be virtually indistinguishable and would pass similar quality control testing. Yet, none of these parameters would reveal that the repeating milling pattern in the second surface could create leak paths between mating surfaces.

Figure 2. Parameters such as Ra, Rz and Wt have identical values for both surfaces yet offer little insight into how the parts will perform.Figure 2. Parameters such as Ra, Rz and Wt have identical values for both surfaces yet offer little insight into how the parts will perform.Digital Metrology Solutions

New surface texture filters provide insight into function

In recent years, new parameters have been developed that closely correlate with particular functionalities. Engineers can use these functional parameters to compare design options during development and then specify these parameters on prints to improve component performance and longevity.

Figure 3. A closing filter acts as a virtual gasket placed onto the test surface, pressing into the peaks and leaving voids below. An opening filter exposes peaks that are sharper than a given radius, which may concentrate stress.Figure 3. A closing filter acts as a virtual gasket placed onto the test surface, pressing into the peaks and leaving voids below. An opening filter exposes peaks that are sharper than a given radius, which may concentrate stress.Digital Metrology SolutionsOne such functional analysis is based on using surface texture filters that can simulate the shape of a mating surface. Figure 3 shows morphological opening and closing filters applied to a surface. The filter in these examples is the path of a virtual circle (or ball for 3D measurements) of a given radius that is moved along the surface data. For the closing filter, the virtual ball rides along the surface. A closing filter acts as a virtual gasket that follows the peaks and leaves voids in the regions of the valleys. By analyzing the gaps created by the closing filter, potential leakage under an actual gasket can be predicted.

For an opening filter, the ball is essentially pushing up from below the surface. The opening filter highlights the peaks in a surface that are sharper than a given radius. These sharp peaks can be related to oil film penetration points or cosmetic defects, or stress concentrations that could lead to cracking.

One crucial aspect of this type of analysis is that both closing and opening filters can be adjusted in real time, letting engineers explore how changes in materials, processes and tolerances might improve performance. For example, the radius of the closing filter can be altered in real time. With a smaller radius, the ball will enter more valleys, thereby modeling a more compliant gasket material. Raising the cutoff wavelength for the waviness profile will increase how much the virtual gasket will “crush” peak material. 

This article focuses more attention on the closing filter. 

The path of the closing filter (as shown in black in Figure 3) may be an interesting presentation of the amount of deformation to be expected by a seal or gasket. However, the difference between the measured surface and closing filter is much more important: It is a direct representation of the cross-sectional leakage area. 

This surface texture analysis method provides direct modeling of the sealing interface. Thus, it is a valuable method for understanding and controlling the root causes of leakage.

Case study: Using a closing filter to predict leakage

In Figure 2, the two surfaces shown are indistinguishable by common surface texture parameters. In Figure 4, these surfaces are revisited with a different analysis approach. A closing filter, or virtual gasket (shown in black), with a radius of 5000 mm, has been applied to the waviness profile (red) for both surfaces. Figure 4. Standard surface texture parameters could not discern a difference between these two surfaces. However, the Wvoid parameter, generated based on a closing filter, shows more than nine times the leak area in the second profile relative to the first surface.Figure 4. Standard surface texture parameters could not discern a difference between these two surfaces. However, the Wvoid parameter, generated based on a closing filter, shows more than nine times the leak area in the second profile relative to the first surface.Digital Metrology Solutions

When the closing filter is applied to the first surface, it closely follows the waviness profile. Although the waviness is quite large in terms of total height, there are no abrupt changes that would cause significant leak areas.

However, when the same closing filter is applied to the second surface, large, periodic voids are shown that cannot be sealed by the virtual gasket. These voids are of concern.

While the profile graphs clearly show the difference in sealing, a numerical value to indicate the amount of leakage could be desirable to specify and control these critical surfaces. The functional parameter Wvoid (void area per unit length) was developed to satisfy that need. Wvoid, which is shown via the OmniSurf software in Figure 4, can be tracked as a measure of sealing quality. The parameter is normalized per unit length, making it independent of the evaluation length and therefore more repeatable and stable. 

This analysis can be extended into three dimensions as well. Figure 5 shows the application of a closing filter to a measured surface using interactive filtering tools in OmniSurf3D software. In Figure 5, the closing filter has been applied across the dataset (on left) to create the closing surface, shown in transparent blue. On the right, the gaps between the closing surface and measured surface are shown as a void surface, which represents the void areas where leakage may occur. As with the 2D analysis, changing the radius of the closing filter can be related to changes in conformability and sealing forces.

Figure 5. On the left, a closing filter (transparent blue) interacts with the measured surface. The difference between the closing surface and measured surface creates the void surface (shown on the right). The void surface represents the void areas where leakage may occur.Figure 5. On the left, a closing filter (transparent blue) interacts with the measured surface. The difference between the closing surface and measured surface creates the void surface (shown on the right). The void surface represents the void areas where leakage may occur.Digital Metrology Solutions

Using Pit and Porosity analysis to trace leak paths

Other advanced analyses can help determine which voids in a surface may link up to form leak paths. In Figure 6, an interactive Pit and Porosity analysis (shown in OmniSurf3D software) has been applied to a surface to analyze the voids, or pores, which may lead to leakage. The analysis shows a 3D map of surface heights on the left, the material ratio curve in the middle, and a plot of pores and leak paths on the right.Figure 6. Pit and Porosity analysis helps determine which voids in a surface may become leak paths.Figure 6. Pit and Porosity analysis helps determine which voids in a surface may become leak paths.Digital Metrology Solutions

The porosity features shown in red are considered closed pores, meaning that they are completely enclosed by material at this given cutting plane. Blue features are open pores, meaning they open onto the edge of the data set. The green regions may be the most important. These are edge-connected regions, representing potential leak paths that are open along either the X or Y axis of the data set. 

At any cross-sectional height level, the open and closed pores can be counted. Or, the pit density, the number of pits per square centimeter, can be calculated. Pit density can give a better indication of the surface in general, irrespective of the size of the measurement area. Analysis tools like this make it easy to visualize what is happening and provide traceable numbers.

Conclusion

Surface texture filters and functional parameters make it possible to quickly and easily measure the properties of a surface that are most specifically tied to a desired function. 

A morphological closing filter can provide insight as to how well a surface will seal and provide insight about other functionalities. These filters can be used to explore changes to materials and methods that may address the root causes of sealing issues, as well as provide parameters that can be specified on prints to guide production and track surface quality.  


Mark Malburg Bio Photo Digital Metrology CopyDr. Mark Malburg is a surface metrologist with BSME and MSME degrees from Michigan Technological University and a doctorate from the University of Warwick. Dr. Malburg spent 10 years in industry prior to forming Digital Metrology Solutions in 1999. Digital Metrology provides custom metrology software development, training and consulting, and it has developed numerous surface texture analysis software packages including OmniSurf, OmniSurf3D and OmniRound. Dr. Malburg’s work, mathematics and software are incorporated into many commercial measuring systems. Visit digitalmetrology.com or email info@digitalmetrology.com for more information.

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