It seems like a rather trivial topic, but let’s think about our profile graphs…
The surface texture plot is often more important than the parameter value. Sure, the parameter value is the thing that is toleranced. But when a parameter like RzDIN goes out of tolerance, can you walk out to the manufacturing line and turn the “RzDIN knob”?
In order to control a process, it is likely that you will need to see the surface. Was RzDIN out of tolerance due to dirt on the surface? Was it due to porosity? Maybe noise in the measurement? These questions cannot be answered without a profile graph.
Many people (labs, manufacturing lines) provide profile graphs with their measurements. The good ones – provide consistent, fixed scaling so that the graphs look the same from measurement to measurement. This helps highlight subtle changes. Auto-scaling should only be used as a starting point while you are figuring out what your fixed scales should be.
Take a look at these two profile plots from OmniSurf. Notice any difference?
The above plot on the left is from a milled surface. It has an Ra value of 0.417 µm. The right profile is from an optical flat and it has an Ra value of 0.002 µm. That’s a huge difference!
This difference is more apparent with consistent, fixed scaling as these graphs show:
However, there’s more to this profile plotting topic:
Most people simply plot a roughness profile – after all, roughness is usually the thing that is toleranced on the drawing. However, OmniSurf’s default graph type is “Primary + Waviness”. This is for several good reasons.
1. A roughness graph doesn’t show waviness.
Here’s a roughness profile for a leaking shaft:
Here’s the same data with the primary and waviness profiles plotted. It’s very apparent that waviness is more significant than roughness.
2. The Primary+Waviness graph shows how the filter is working.
With the P+W graph you are able to see if the filtering “fits” the shapes of the profile. The roughness filtering operation takes place on the primary profile. Thus, it makes the most sense to display the filtering operation as applied to the primary profile. In the below example: we can easily see that the 0.8 mm filter cutoff does a better job of following the shape of the surface and thus it will do a better job of describing the shapes that ultimately caused this component to fail.
3. It’s easy for your eye to “subtract”
The roughness profile is “everything that is above and below the waviness profile”. When seeing a graph like this. Your eye can easily see what is above and what is below the waviness profile. There really isn’t a need to even plot roughness!
4. It’s very difficult for your eye to “add”
It is hard to visual how this roughness profile and this waviness profile combine to form the “real” surface:
Putting the two graphs on top of each other doesn’t help very much:
However, if we plot the Primary with Waviness profiles this is our view of the surface:
With this graph we can immediately see:
– The filtered waviness profile is moving up and down with the feedrate… might be time to consider using a longer filter cutoff
– The profile has a general “U-shape,” the middle is lower than the edges. This was very hard to pick out of the other graphs.
– The Primary+Waviness graph gives a much clearer picture of the actual peak-to-valley heights of the profile features. These actual peak-t0-valley heights are much higher than those indicated on the roughness profile graph.
5. Don’t fall for filtering problems
When plotting the roughness profile for a surface with deep scratches or pores we often see high peaks on each side of the scratch or pore.
Sometimes these are real. Sometimes they are caused by the filter being “pulled” into the valleys. The Primary+Waviness plot helps us know for sure:
In this case, the filter is being pulled into the valleys. The areas above the waviness profile become the artificial “peaks” in the roughness profile. This is definitely a case where robust filtering is needed.
Hopefully, this help you make more sense of your profile graphs and ultimately make better decisions based on your measurements!
For more information contact Digital Metrology today!
Most of us that have been around surface metrology have had a reasonable grasp of the “filter cutoff” concept. In the case of surface texture there are long wavelengths that we call “waviness” and short wavelengths that we call “roughness”. The wavelength that separates these two regimes is called the “cutoff wavelength” and a filter is used to separate the profiles. (See the 3 Steps to Understanding Surface Texture for more on surface texture filtering.)
A recent addition to Digital Metrology’s OmniRound software gave some interesting insight into Gaussian filtering for roundness. This has been pretty eye-opening for many people and may surprise you. However, some of you many need some background first…
But first some background…
In the case of roundness we don’t necessarily talk about “wavelengths” like we do in surface texture – instead we talk about “frequencies”. Roundness is a low-frequency “form” measurement so high frequency “roughness” effects are removed. Instead of a cutoff wavelength indicating how much high-frequency roughness to remove, we have a cutoff frequency expressed in terms of “undulations per revolution” or “upr”. An “undulation” can be thought of as a “wave”. In fact, in older documents we see the term “cycles per revolution” or “cpr”. In today’s world this term has become upr.
So what frequencies do we include in roundness measurement?
Historically, the default roundness cutoff was 50 upr. This means that anything occurring less than 50 times per revolution is considered to be the “shape” of the surface. If it occurs more than 50 times, then it is considered to be in the roughness regime – not roundness. Recent ISO standards give cutoff recommendations based on the conversion of wavelengths to cutoff frequencies based on the circumference of the part. Ultimately, the designer needs to consider which frequencies are of interest and choose the cutoff accordingly. ISO and national standard propose the “preferred series” of cutoffs to be 5, 15, 50, 150, 500, 1500, etc. This way, the instrument manufacturers and users have a basic set to choose from. Check out a US Quarter at different cutoff frequencies (First row left-to-right: 5, 15, and 50 upr. Second row: 150 and 500 upr):
In the case of the quarter, a “default” 50 upr cutoff (rightmost graph on the top row) would not include serrations on the quarter in the roundness evaluation. By the way, if you don’t feel like counting them, there are 119 serrations.
Choosing a smaller cutoff value means that the data will be “smoothed” more. This smoothing occurs via a Gaussian filter which is a weighted moving average and the width of the moving average depends on the cutoff value.
Brick walls and ripples
The Gaussian filter is not a perfect filter in terms of separating frequencies. For example, if you choose a 50 upr filter. It does not mean that you will get everything up to 50 upr and nothing beyond 50 upr. That kind of filter that give 100% of low frequencies and 0% of high frequencies is referred to as a brickwall filter and has this characteristic:
Unfortunately, the brick wall filter causes extra “ripples” to be put into the roundness data. These ripples are due to what’s called the “Gibbs Effect”. (You can Google it for more information.)
To get rid of these extra ripples, we need reduce the transmission at some of the lower frequencies and add some transmission at some of the higher frequencies. When we do this, it makes the transmission less “sharp”. The Gaussian filter is an “ideal filter” in that it has sharpest possible transmission while not adding any additional “ripples” to the profile. For you controls people, you can think of the Gaussian filter as “critically damped”.
Here’s how the Gaussian transmission characteristics look for some common cutoffs.
Now this may surprise you…
The above transmission graph gives the impression that the Gaussian filter is pretty “sharp” in terms of its transmission. However, that graph is typically shown on a logarithmic x-axis. The latest version of OmniRound allows you to see the selected filter’s transmission right on top of the frequency content of your measured profile.
So let’s take what we’ve seen with the quarter (way at the top) and filter it with a typical 50 upr Gaussian filter (like we’ve just seen) and see what the filter transmission looks like.
The light blue curve on the “Harmonic Amplitudes” graph is the transmission characteristic for the selected, 50 upr Gaussian filter. This transmission doesn’t look very sharp but it is, in fact, what the Gaussian filter does whenever you are measuring roundness.
(NOTE: If you need a quick tutorial on the harmonic graph and what the bars mean, click over to the BrakeView website and check out the description at: http://www.BrakeView.com/Harmonics.html )
So the Gaussian isn’t a “sharp” as it appears on log paper. In fact as we look at the OmniRound screenshot above, we see that the transmission is very long and it includes frequencies almost all the way to the serrations. If you think that is interesting, check out what a 15-50 upr bandpass Gaussian filter’s transmission looks like:
In the case of the 15-50 bandpass, the most that any single frequency is transmitted is approximately only 75%!
So what do we do with this?
I know that this is supposed to be a 60-second tutorial and I know I’ve run way over that time limit. But hopefully this is a quick “reality check” for those of you involved in the specification and measurement of roundness.
There is much more that we can talk about in terms of understanding roundness, harmonics, filter types, bandpass analysis and how these relate to making your parts work better.
More information on OmniRound
Or for help on roundness specification and measurement send an email to: firstname.lastname@example.org
A common question in surface measurement is “I have a surface finish specification. Where do I begin?” or “I’m new to the field of surface measurement. What do I need to know?” With that in mind, this tutorial is provided to help you “hit the high points” of surface measurement (no pun intended).
Surfaces are comprised of many “shapes”. We call the long wavelength shapes: “waviness” and the short wavelength features: “roughness”. The measurement of surfaces involves producing numbers to describe these shapes.
By the way, the blue profile in the top graph is referred to as the “primary” profile.
In general, the term “surface texture” refers to the primary profile, roughness, waviness and other surface attributes such as the direction of the surface features (also referred to as the “lay” of the surface). The term “surface finish” typically refers to the “roughness” aspects of the surface – ignoring the shape and underlying waviness. Be careful when dealing with only the “surface finish” as many functional problems are related to waviness as well.
The picture is pretty, but how do we do it?
Surface measurement can be understood through the use of 3 fundamental topics:
Since I’ve only got 45 seconds left, we’d better get started…
The first step in dealing with surface finish or surface “texture” is removing the underlying “shape”. In many cases the surface to be measured is tilted relative to the measuring device. In other cases, the surface may be nominally curved. In either case, the underlying geometry must be removed. This involves the “fitting” of a geometric reference such as a line or an arc and then looking at the wiggles (residuals) above and below the reference geometry.
The raw data from the probe is shown in the top (gray) profile. Superimposed on the raw data is a least squares line. In this case the least squares line is used to remove the tilt from the profile. The residuals (above and below the line) make up the blue (primary) profile.
Note: a small filter is sometimes used to remove noise from the primary profile. This filter is called the “short wavelength filter” but that’s another topic for another day.
Once the geometry has been removed we need to separate the waviness and the roughness. This is the most critical aspect of surface measurement and yet it is probably the least understood.
Filtering surface profiles involves running a “smoothing” filter through the primary data. The amount of smoothing is based on a “filter cutoff wavelength”. The “cutoff wavelength” is the wavelength that separates roughness from waviness. Shorter wavelengths fall into the roughness profile and longer wavelengths appear in the waviness profile.
A “Gaussian” filter is recommended in ASME and ISO standards. Gaussian filters are based on passing a Gaussian, weighted average through the primary profile – resulting in the waviness profile. The roughness profile is made up of all of the peaks and valleys (residuals) above and below the waviness profile.
Changing the filter cutoff value (which changes the amount of “averaging” and “smoothing”) can have a huge impact on the measurement of roughness and waviness. Choosing a smaller cutoff value will result in smaller roughness values… even though the real surface could be very rough. The filter cutoff provides the means for defining “what I am calling roughness”.
The following graphic presents the same surface with two different filter cutoffs. The roughness profile on the bottom left gives twice the “average roughness” (Ra) value of the profile on the bottom right.
There is a table of “standard” cutoff values (along with selection recommendations) in ASME B46.1-2002 as well as ISO 4288-1996. This information is also provided in OmniSurf’s help system.
OK – I know it. This is much more than 60 seconds. But there was a lot of good stuff to talk about.
Once we’ve separated things into roughness and waviness profiles we need to come up with numbers to describe them. After all, pictures are great, but engineers love numbers. The simplest of parameters is the “total” height of a given profile. This is the “peak-to-valley” height of the profile. For the primary profile the total, peak-to-valley height is designated: “Pt”. For the waviness profile it is “Wt” and for the roughness profile it is “Rt”. (The first letter always designates the profile.)
Unfortunately, the old adage “you get what you pay for” holds true here. The parameters, Pt and Rt are often quite unstable since they can be influenced by dirt, vibration and other things that are “outside the normal statistics” of the surface. On the other hand, the peak-to-valley waviness, Wt, is considerably more stable as it is based on only the long wavelengths and effects such as dirt are “smoothed out”.
Regarding roughness parameters, hundreds of parameters have been proposed. We won’t go into all of them here because, after all, we have gone well past our 60 seconds.
The most common roughness parameter is the average roughness, Ra. Many years ago this parameter was referred to as the “arithmetic average” (AA) or the “centerline average” (CLA). Today we designate it “Ra” to be consistent with all of the rest of the parameters.
The average roughness (Ra) reports the “average distance between the surface and the meanline” looking at all of the points along the profile.
For example, if a surface has heights and depths as follows, it will give an Ra value of 3.33 (in units of height such as microinches or micrometers):
Since the average roughness (Ra) is simply the “average distance” from the meanline, peaks and valleys are treated the same way. So several profiles can all have the same Ra value:
Second to Ra, in terms of popularity is the “average peak-to-valley roughness” or “ten-point roughness”, designated Rz. Rz has different definitions based on the standard that you are working with. However there are two basic definitions: one used in German (DIN) standards (which is in today’s ASME and ISO standards) and one used in Japanese (JIS) standards which was used in older ASME and ISO standards. There is no time left to discuss these in great detail, but it can be said that the DIN approach uses one peak and one valley in each sampling length, whereby the JIS approach uses 5 peaks and 5 valleys in each sampling length. As a result the DIN values are always equal to or higher than the JIS values. Be sure that you know which one you are using!
There is a lot more to talk about (but we’re out of time!)
That’s why Digital Metrology offers on-site training for surface texture specification, measurement and analysis.
Contact Digital Metrology Solutions today!