Surface roughness
From a mathematical perspective it is related to the spatial variability structure of surfaces, and inherently it is a multiscale property.
It is quantified by the deviations in the direction of the normal vector of a real surface from its ideal form.
Roughness is typically assumed to be the high-frequency, short-wavelength component of a measured surface.
However, in practice it is often necessary to know both the amplitude and frequency to ensure that a surface is fit for a purpose.
Roughness plays an important role in determining how a real object will interact with its environment.
Roughness is often a good predictor of the performance of a mechanical component, since irregularities on the surface may form nucleation sites for cracks or corrosion.
Generally speaking, rather than scale specific descriptors, cross-scale descriptors such as surface fractality provide more meaningful predictions of mechanical interactions at surfaces including contact stiffness[1] and static friction.
[2] Although a high roughness value is often undesirable, it can be difficult and expensive to control in manufacturing.
For example, it is difficult and expensive to control surface roughness of fused deposition modelling (FDM) manufactured parts.
This often results in a trade-off between the manufacturing cost of a component and its performance in application.
These can be of the contact variety (typically a diamond stylus) or optical (e.g.: a white light interferometer or laser scanning confocal microscope).
For example, a gloss surface can be too shiny to the eye and too slippery to the finger (a touchpad is a good example) so a controlled roughness is required.
Surface structure plays a key role in governing contact mechanics,[1] that is to say the mechanical behavior exhibited at an interface between two solid objects as they approach each other and transition from conditions of non-contact to full contact.
In particular, normal contact stiffness is governed predominantly by asperity structures (roughness, surface slope and fractality) and material properties.
In terms of engineering surfaces, roughness is considered to be detrimental to part performance.
An exception is in cylinder bores where oil is retained in the surface profile and a minimum roughness is required.
is by far the most common, though this is often for historical reasons and not for particular merit, as the early roughness meters could only measure
[7] The MOTIF method provides a graphical evaluation of a surface profile without filtering waviness from roughness.
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.
With modern digital equipment, the scan can be evaluated to make sure there are no obvious glitches that skew the values.
Because it may not be obvious to many users what each of the measurements really mean, a simulation tool allows a user to adjust key parameters, visualizing how surfaces which are obviously different to the human eye are differentiated by the measurements.
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.
is the arithmetic average value of filtered roughness profile determined from deviations about the center line within the evaluation length and
These parameters are often used to describe repetitive roughness profiles, such as those produced by turning on a lathe.
The mathematician Benoît Mandelbrot has pointed out the connection between surface roughness and fractal dimension.
[11] The description provided by a fractal at the microroughness level may allow the control of the material properties and the type of the occurring chip formation.
But fractals cannot provide a full-scale representation of a typical machined surface affected by tool feed marks; it ignores the geometry of the cutting edge.
Across multiple fields, connecting physical, electrical and mechanical behavior with conventional surface descriptors of roughness or slope has been challenging.
These are then digitally stitched together using relevant software, resulting in a 3D image and accompanying areal roughness parameters.
