What is the right sequence for a Gage R&R study?

G

Gagandeep S. Datta

For a gage R&R study, is the following sequence correct?

Resolution
Bias
Linearity
Calibration
Repeatability
Reproducibility
Stability

Regards,
Gagandeep
 

bobdoering

Stop X-bar/R Madness!!
Trusted Information Resource
I would always do calibration first - which generally includes bias and linearity. Otherwise, the other studies could be meaningless and a waste of time.

Calibration
Bias (part of calibration)
Linearity (part of calibration)
Repeatability (gage R&R)
Reproducibility (gage R&R)
Resolution (ndc from gage R&R)
Stability (longer term study)
 
C

Coleman Donnelly

I am digging deeper into MSA in general right now and have some questions about the method of the studies mentioned here.

In my example I am evaluating a CMM program which essentially becomes my gage. Now there is no real way to calibrate my program based on traditional methods so I am trying to better understand how I can use the tools within MSA to improve and qualify my results.

Isn't it wrong to assume the calibration of my CMM will satisfy the Bias and Linearity concerns within my program since the calibration procedure will be using the gage under ideal circumstances while the program challenges the equipment in a different way?

I would argue the same rules apply with a drop indicator. Verifying an indicator with gage blocks does not necessarily mean that I should ignore the effects that the operator, fixture, part and environment all play on the studies mentioned. I am struggling with these concepts as well.

It seems to establish bias which is needed for linearity I need a known reference artifact that my program can measure which I need to use in order to establish the known reference value... I’m stuck in a loop here and I can’t find the way out...

Any suggestions?
 

Bev D

Heretical Statistician
Leader
Super Moderator
A CMM is like a functional tester.
Calibrate the components - are they the right size, etc.
then use the CMM to measure known NIST tracable standards.
If your software dosn't do the math right you will fail the calibration.
If you pass the calibration, your software (the calcualtion portion) also passes calibration.

Then proceed to R&R...

don't overthink it, try it.
 
C

Coleman Donnelly

I understand where you are coming from - however the CMM is not functioning in the same manner for the calibration as it is during the inspection program.

During A2LA calibration the machine inspects reference artifacts using single point to point measurements on features with close to perfect form.

During my program I am scanning thousands of data points using varying speeds and force which will have an effect on my probe bending especially since the form of the part is not perfect and my CMM is constantly compensating the probe route to match the surface I am measuring. I don't feel that I can ignore these contributing factors. Just because my machine past calibration does not mean that I can’t generate bias downstream does it? And if I can influence bias don't have to check for it?

I can agree that if I verify that my machine is within a certain accuracy requirement that my BIAS and my LINEARITY for the CMM are acceptable. But how do I at least demonstrate that it does not have an effect now that I am using that gage in a different manner?
 
Q

qualitytrec

linearity and bias are calculated by the CMM calibration tech and should be reported to you on the report.
The CMM should be functioning in the same way if your calibration tech is doing his job. The use of properly restrained standards to check linearity and bias is to validate your real life measurements.
The variable comes from improperly restrained parts and improper programming techniques and/or from using a touch probe where a vision or lazer system is needed.
Learning what is most important to all stakeholders is necessary to properly inspect a part.
If the above are considered and evaluated you should be acceptable in most applications.

Not sure if this helps you or not.
Mark
 

Bev D

Heretical Statistician
Leader
Super Moderator
I understand where you are coming from - however the CMM is not functioning in the same manner for the calibration as it is during the inspection program.

But how do I at least demonstrate that it does not have an effect now that I am using that gage in a different manner?

OK - so now I better understand your dilemma.
This is a true situation for ALL measurement systems. you can pass calibration but when you use the system in real life, with real parts, real operators, real fixtures, real environment etc. you may be 'inaccurate' or biased or have a non-linear response.

Unfortunately most of the common literature and 'standards' regarding this are rather quiet about how to assess truth vs results in a real use environment.

However, the method for assessing this is rather simple (although not always easy). It commonly referred to as method comparison. It would be a special case of method comparison where the system beign assessed is compared to some 'true' method.
  1. Determine a 'gold standard' method
  2. Select real parts or even a handful of 'gold parts' or NIST standards that span the range of teh dimension or property you will be measuring (if you must).
  3. Measure each of the parts TWICE with EACH measurement system. You should randomly select which parts get measured with which system IF time is a factor.
  4. Assess repeatability of each system. There can be no real assessment of bias until repeatability is understood.
  5. Now take the first measurement of each part for each system and compare the results. I use a Youden plot and the Bland Altman method to look at and quantify the bias and linearity of the 'production' system vs. 'truth'

If you check out the attached docuemnt it will provide some detailed explanations, formulas and references.
 

Attachments

  • MSA V and V.doc
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C

Coleman Donnelly

I think you are now on the track that I am trying to better understand... Im not looking to keep it simple - Im looking to do it right. I want to get a better understanding of measuremeant uncertainty and it seems that Bias and Linearity are an obvious part of that equation.

Can you give any guidance on the method of comparison and how it works so that I can better understand what you are suggesting and how it relates to a dimensional layout of parts?
 
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