Gage R&R TMV Acceptance Criteria

#1
A client of mine has been using Gage R&R to validate their test methods (we are in the medical device industry). Some of their test protocols specify to use a mix of known good and known bad parts (which, yes, is good), but because their acceptance criteria is <30% %Tol they always exclude the results of the known bad part(s) from their analysis since Minitab will not calculate %Tol when you have samples outside of the specification limit(s). I was exploring the possibility of setting up their studies to analyze the known good and known bad parts separately (and have them look at %Study Var for the known bad instead), but since samples are always chosen at random there are instances where there may be only 1 known bad part. In such case, it doesn't appear that I can run an adequate Gage R&R analysis with only 1 part.

My questions are:
1. What metric should I set my acceptance criteria to in general (%Tol, %Study Var, etc.)? Is there a way to run my Gage R&R analysis on the known good and known bad parts together, or is it okay for me/do I need to run them under two separate analyses?
2. How can I address the situation of having only 1 known bad part - is there a good way to do Gage R&R and look at a different RR metric, or should I write our test method validation protocol such that we always have more than 1 known bad part (or known good)?
 
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Miner

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#2
What version of Minitab are you using? I just used V18 and tightened the tolerances such that parts were outside both the upper and lower specifications, and Minitab 18 performed the analysis for %Tolerance without a problem.

%Tolerance is used when the device will be used for inspection. %Study Variation is used when the device will be used for SPC or capability studies. The only time both are used is when the device will be used for inspection and SPC/capability studies.

When you use %Tolerance, the part selection does not affect the results, so there is no need to specifically select bad parts. When you use %Study Variation, part selection is critical and MUST reflect the typical process variation. Purposely selecting parts from the full tolerance range, or outside the tolerance range will distort the process variation and make %Study variation meaningless.
 
#3
What version of Minitab are you using? I just used V18 and tightened the tolerances such that parts were outside both the upper and lower specifications, and Minitab 18 performed the analysis for %Tolerance without a problem.

%Tolerance is used when the device will be used for inspection. %Study Variation is used when the device will be used for SPC or capability studies. The only time both are used is when the device will be used for inspection and SPC/capability studies.

When you use %Tolerance, the part selection does not affect the results, so there is no need to specifically select bad parts. When you use %Study Variation, part selection is critical and MUST reflect the typical process variation. Purposely selecting parts from the full tolerance range, or outside the tolerance range will distort the process variation and make %Study variation meaningless.
I'm using Minitab version 20.1. There are some instances where I was able to get % Tol when I include failing samples, but other instances I receive the following error from Minitab: "* NOTE * The average measurement is not less than the upper spec limit, indicating the measurements are very far from the target. No %Tolerance is calculated"

In such a case, I'm dealing with a leak rate test where the test results are logarithmic with results varying between e-10 to e-5 depending on the product. Is there a different way I should be performing the Gage R&R on a logarithmic dataset? For reference, I've attached my raw data here.

Since the gage is used for inspection, it sounds like %Tol is the appropriate acceptance metric. What is the rationale for part selection not affecting the results when using %Tol? I'm thinking one option I might have is to perform a Gage R&R analysis (without bad parts) and separately perform an Attribute Agreement test to demonstrate that my gage is capable of differentiating bad/good samples for my test method validation. As such, am I subject to my risk-based sample size requirements for the Attribute Agreement test? (i.e., for a 95/95 confidence/reliability wherein my sampling procedure requires 59 data points, do I then need 59 data points for this? If so, can I justify pooling data points instead of getting an exorbitant amount of physical samples to test?)
 

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#4
The logarithmic nature of the data is definitely having an adverse impact on the analysis. When you eliminate the results for parts 5, 6 and 8, the R&R results are realistic and you are able to obtain the %Tolerance. I will continue to investigate this. I don't like to use attribute agreement analysis when you have continuous data.

Regarding the part selection, normally measurement variation is constant across part sizes (at least within the tolerance range). Therefore, the only factors included in the calculation are the tolerance and the measurement variation. Part variation is not part of the calculation. For the %SV, part variation is included, so you must be careful to select parts that reflect actual process variation, not deliberately selected extremes.
 
#5
The logarithmic nature of the data is definitely having an adverse impact on the analysis. When you eliminate the results for parts 5, 6 and 8, the R&R results are realistic and you are able to obtain the %Tolerance. I will continue to investigate this. I don't like to use attribute agreement analysis when you have continuous data.

Regarding the part selection, normally measurement variation is constant across part sizes (at least within the tolerance range). Therefore, the only factors included in the calculation are the tolerance and the measurement variation. Part variation is not part of the calculation. For the %SV, part variation is included, so you must be careful to select parts that reflect actual process variation, not deliberately selected extremes.
Thank you, let me know if you find a solution to performing the analysis with logarithmic data.

I, too, prefer a variable analysis with continuous data, but I need to establish that my measurement system is capable of differentiating good/bad samples. The best way I can think of establishing this is through some attribute agreement analysis.
 
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