Surface Roughness

Surface roughness, often shortened to roughness, is a measure of the texture of a surface. It is quantified by the vertical deviations of a real surface from its ideal form. If these deviations are large, the surface is rough; if they are small the surface is smooth. Roughness is typically considered to be the high frequency, short wavelength component of a measured surface (see surface metrology). However, in practice it is often necessary to know both the amplitude and frequency to ensure that a surface is fit for purpose.

Roughness plays an important role in determining how a real object will interact with its environment. Rough surfaces usually wear more quickly and have higher friction coefficients than smooth surfaces. Roughness is often a good predictor of the performance of a mechanical component, since irregularities in the surface may form nucleation sites for cracks or corrosion. On the other hand, roughness may promote adhesion.

Although roughness is often undesirable, it is difficult and expensive to control in manufacturing. Decreasing the roughness of a surface will usually increase exponentially its manufacturing costs. This often results in a trade-off between the manufacturing cost of a component and its performance in application.

Roughness can be measured by manual comparison against a “surface roughness comparator”, a sample of known surface roughness, but more generally a Surface profile measurement is made with a profilometer that can be contact (typically a diamond styles) or optical . However, controlled roughness can often be desirable. For example, a gloss surface can be too shiny to the eye and too slippy to the finger (a touchpad is a good example) so a controlled roughness is required. This is a case where both amplitude and frequency are important.

A roughness value can either be calculated on a profile (line) or on a surface (area). The profile roughness parameter (Ra, Rq,…) are more common. The area roughness parameters (Sa, Sq,…) give more significant values.

There are many different roughness parameters in use, but Ra is by far the most common though this is often for historical reasons not for particular merit as the early roughness meters could only measure Ra.

Other common parameters include Rz, Rq, and Rsk. Some parameters are used only in certain industries or within certain countries.

Since these parameters reduce all of the information in a profile to a single number, great care must be taken in applying and interpreting them. Small changes in how the raw profile data is filtered, how the mean line is calculated, and the physics of the measurement can greatly affect the calculated parameter. With modern digital equipment it makes sense to look at the scan and make sure there aren’t some obvious glitches that are skewing the values – and if there are, to re-measure.

Because it is not obvious to many users what each of the measurements really means, it is helpful to have a simulation tool that lets you “play” with key parameters and see how well (or badly) surfaces which are obviously different to the human eye are differentiated by the measures. It is clear, for example that Ra would fail to distinguish between two surfaces where one is composed of peaks on an otherwise smooth surface and the other is composed of troughs of the same amplitude. Such tools can be found in app format.

By convention every 2D roughness parameter is a capital R followed by additional characters in the subscript. The subscript identifies the formula that was used, and the R means that the formula was applied to a 2D roughness profile. Different capital letters imply that the formula was applied to a different profile. For example, Ra is the arithmetic average of the roughness profile, Pa is the arithmetic average of the unfiltered raw profile, and Sa is the arithmetic average of the 3D roughness.

Amplitude parameters characterize the surface based on the vertical deviations of the roughness profile from the mean line. Many of them are closely related to the parameters found in statistics for characterizing population samples. For example, Ra is the arithmetic average of the absolute values and Rt is the range of the collected roughness data points.

The roughness average, Ra is the most widely used one-dimensional roughness parameter.