What does the Gage R&R results tell about my Accept/Reject Decisions?

T

Tim54

#1
Need some help in applying the gage R&R. Let's say I've conducted my gage R&R study and have the results and I know my EV, AV, and R&R percentages using the avg/range method. Now, how do I use/apply that to 'real world", i.e. what is the relationship of the results to the actual specification tolerance? for example, using the QI macros AIAG Ford test data:
my tolerance is 2
% using TV, EV = 9.0%, AV = 5.0%, R&R = 10.3%
% using tolerance, EV = 7.2%, AV = 4.0%, R&R = 8.3%
What is this telling me about the accept/reject decisions that I'm making for this theoretical product? thanks for your help.:confused:
 
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T

Tim54

#3
thanks for the response. I guess what I'm trying to understand, but didn't know how to express it is, how do you or can you factor in the gage r&r % to your regular inspection results, i.e. can you determine how much is the gage error causing us to reject good parts (alpha risk)or accept/release bad parts (beta risk). thanks
 

Miner

Forum Moderator
Staff member
Admin
#4
Use the Search > Advanced Forum Search option above.

Enter "Performance Curve" into the keyword field and restrict the search to the Gage R&R sub-forum. The search results will provide several threads and attachments that will provide the information for which you are looking.
 
B

Barbara B

#5
The gage performance curve will give you the probability for accepting a good part. It is drawn for 1 single part with known value as you need the bias = measurement - known.value.

For a Gage R&R study there are two different risks for misclassification: joint probability and conditional probability. The first gives you two probabilities for the combination of "part is good & will be rejected" and "part is bad & will be accepted". The latter is calculated a little bit differently and gives you the two probabilities for "part will be rejected under the condition part is good" and "part will be accepted under the condition part is bad" (see attached screenshot from the Minitab help).

Both kinds of probabilities could be calculated in Minitab
Stat > Gage R&R (crossed) > Options
after filling in values for the specification limits and checking "Display probabilities of misclassification". Note: The method for the analysis has to be ANOVA. Xbar-R doesn't give you the chance for calculating risks of misclassification.

Best regards,

Barbara
 

Attachments

Bev D

Heretical Statistician
Staff member
Super Moderator
#6
a consideration beyond the statistical probabilities and calculations is the larger concept of what it means to run at or near a specification limit. Most specifications are nto engieered hard lines in the sand where all is happy when wthin the lines and all is horrible when you are outside the lines. Many times we are dealign with either stackup conditions, future deterioriation or variation due to environmental conditions. Because of this we are better off to focus our efforts away from the 'mathematical probabilties' of a false acceptance or false rejection from measurement error and towards improving our process so that we are not running at the limits.

If we really need to understand the false accept/reject rate for some reason (such as justifying work to improve the process or guardbanding of some type) my experience has shown me that emperical categorical studies of parts near the limits are far better at quantifying the rate in such a way that managers can believe the answer where the statistical approach tends to make their eyes glaze over...
 
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