Capability Studies for Profile (GD&T Callout)

A

ahshah

#1
Any threads/discussions on Capability studies for Profile and Position? I did find the Paul's PPK speadsheets, but looking for additional input/discussions.
 
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S

Sturmkind

#2
Hi, Ahshah!

There have been several previous good threads: Cpk Studies for Concentricity - Dimension that is spec'd 0.0 - .03 (
1 2). Use the Search function on the top tool bar next to the Orange 'Quick Links'.

Good luck!

P.S. The regimen and rationale are the same for unilateral tolerances like flatness, true position (though this must be treated as a bi-modal distribution), profile (which arguably could also be treated as a bi or tri-modal distribution). They may, or may not be normal and depending on distance from the specification limits may, or may not have good CP/CR.
 
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Paul F. Jackson

Quite Involved in Discussions
#3
If I had a profile callout to manage...
I would study the given process,
identify (to the best of my ability) the vulnerabilities of the process in contributing
variation to the feature profile,
examine the how the process can be adjusted,
assess what tools are at my disposal to measure the profile deviation,
and finally reconcile how the profile deviation affects function...

then I would choose strategic points on the profile that are:
sensative to the process vulnerabilities
oriented (if possible) to the available individual process adjustments
and influential to the products functional variation.

I would attempt to achieve control on each of the points individually and adjust the means in the process (if possible) to my production sensative targets.
Check my profile assumptions... process vulnerabilities for variation, effects of process adjustment, and impact on functional variation.

Then I would run a production representative sample, predict the initial capability of the process based on the simple +/- mean deviation from the profile target, and report the capability of these strategic points... highlighting the worst as the predicted capability.

Paul
 

Miner

Forum Moderator
Staff member
Admin
#5
In addition to the previous advice, consider the following:

Adjacent points in a profile will exhibit a behavior that is analogous to autocorrelation. In case you are not familiar with autocorrelation, it is a time series where the value of a number of subsequent measurements depends on the value of a prior measurement. For example, the ambient temperature at 11:00 AM depends on the temperature at 10:50 AM. It is probably also dependent on the temperature at 10:30 AM, but at some time difference, the dependence breaks down.

While not true autocorrelation, the analogy should hold true. Two points on a profile made by a specific process will tend to depend on the other the closer the proximity. The practical aspect of this is that control of a single point will control a band of the profile surface. The width of the band will depend on the process that created the surface. An autocorrelation analysis may be able to define the width of this band for a given process.

This would justify the practice of using a few points to control a surface.
 

bobdoering

Stop X-bar/R Madness!!
Trusted Information Resource
#6
Adjacent points in a profile will exhibit a behavior that is analogous to autocorrelation. In case you are not familiar with autocorrelation, it is a time series where the value of a number of subsequent measurements depends on the value of a prior measurement. ... While not true autocorrelation, the analogy should hold true.

This would justify the practice of using a few points to control a surface.
This is a great example of the dire need to develop the total variance equation for each situation. I agree, adjacent points are related. Also, without a doubt, there is autocorrelation from the tool wear (so we can rule out independent, random variation for that participant of variance.) But, as an example, if a position callout is from an inside dimension to an outside datum, the the tool wear on the outside may (and most likely does) wear at a different rate then the tool on the inside. Also, if there is a need to re-chuck to generate one dimension after the other, then the error in chucking will participate (and, it may very well be random and independent).

The worst thing to do is to look at any variation as 'one output'. It is a multi-modal output, and how many modes that are significantly influencing the capability will be a function of the CNX analysis - which ones you can adjust - which ones you can keep statistically insignificant, and which ones you have no control over. Those factors also influence the validity of any representation you prepare (capability index, for example) over time. As much as people would love plug and chug - or use it indiscriminately - it is simply not that easy. Some people have proposed using nonlinear response surface modeling to describe the probability of these types of relationships. Sounds interesting, but I have not tried it yet. In any event - one would have to look at each callout, and the processes that generate it, very specifically - it is nearly impossible to generalize.
 
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P

Peter Truitt

#7
Are folks familiar with SmartProfile? If provides tolerance analysis per ASME Y14.5-1994.
 
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