Another common question with Gage Crossed is what table to look at when assessing your measurement system. By default, Minitab gives a %Contribution table and %Study Variation table. Which one should you use when assessing where the variation is mostly coming from? Well, you could use either of them.
The %Contribution table can be convenient because all sources of variability add up nicely to 100%. Example:
The %Study Variation table doesn’t have the advantage of having all sources add up nicely to 100%, but it has other positive attributes. Because standard deviation is expressed in the same units as the process data, it can be used to form other metrics, such as Study Variation (6*standard deviation), %Tolerance (if you enter in specification limits for your process), and %Process (if you enter in an historical standard deviation). Of course, there are guidelines for levels of acceptability from AIAG as well:
If the Total Gage R&R contribution in the %Study Var column (% Tolerance, %Process) is:
- Less than 10% - the measurement system is acceptable.
- Between 10% and 30% - the measurement system is acceptable depending on the application, the cost of the measuring device, cost of repair, or other factors.
- Greater than 30% - the measurement system is unacceptable and should be improved.
If you are looking at the %Contribution column, the corresponding standards are:
- Less than 1% - the measurement system is acceptable.
- Between 1% and 9% - the measurement system is acceptable depending on the application, the cost of the measuring device, cost of repair, or other factors.
- Greater than 9% - the measurement system is unacceptable and should be improved.
We field a lot of questions about %Tolerance as well. %Tolerance is just comparing estimates of variation (part-to-part, and total gage) to the spread of the tolerance.
When you enter a tolerance, the output from your gage study will be exactly the same as if you hadn't entered a tolerance, with the exception that your output will now contain a %Tolerance column. Your results will still be accurate if you don't put in a tolerance range; however, including the tolerance will provide you more information.
For example, you could have a high percentage in %Study Var for part-to-part, and a high number of distinct categories. However, when you compare the variation to your tolerance, it might show that in reference to your spec limits, the variation due to gage is high. The %Tolerance column may be more important to you than the %Study Var column, since the %Tolerance is more specific to your product and its spec limits.
Think of it this way: Your total variation comprises part-to-part and the gage (Reproducibility and Repeatability). After adding a tolerance, we get to see what percentage of variation really dominates within the tolerance bounds specified. If the ratio between the Total Gage R&R and the tolerance is high (%Tolerance>30%), that provides insight about the types of parts being selected. It’s telling us that the measurement tool cannot effectively decipher if the part is good or bad, because too much measurement system variation is showing up between specifications.
I hope the answers to these common questions help you next time you’re doing Gage R&R in Minitab!
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What does it mean when % Study Var is high and % Tolerance is low for Total Gage R&R and distinct categories is 1?
Study Var %Study Var %Tolerance
Source StdDev (SD) (5.15 * SD) (%SV) (SV/Toler)
Total Gage R&R 0.139729 0.719606 100.00 7.20
Repeatability 0.139729 0.719606 100.00 7.20
Reproducibility 0.000000 0.000000 0.00 0.00
Operator 0.000000 0.000000 0.00 0.00
Part-To-Part 0.000000 0.000000 0.00 0.00
Total Variation 0.139729 0.719606 100.00 7.20
Number of Distinct Categories = 1
Thank you.
With respect to your distinct categories being 1, this means that "the measurement system is of no value for controlling the process", according to AIAG. Here is a link to an article that discusses NDC in more depth: http://www.minitab.com/en-US/support/answers/answer.aspx?id=276
The %tolerance is calculated by comparing the estimates of variation to the spread of the tolerance. So, the wider the spread, the smaller the %tolerance value will be. You will notice that regardless of what specs you put in, the %Study Var will remain the same, as it is calculated from the raw data.
I hope this helps! Give us a call if you have further questions: 814-231-2682.
>90%
http://www.itl.nist.gov/div898/handbook/mpc/mpc.htm
Don Wheeler has attacked the AIAG methods in many ways, and his Evaluation of the Measurement Process (EMP) methods as well as the AIAG methods are explained and supported in JMP v. 10 now. MInitab should perhaps do the same.
Measurement Systems Analysis is trivialized by focus on the AIAG %GRR numbers, as AIAG admits in its new manual. Bias, Linearity, Stability, are key issues. And the PURPOSE of using a gage is important. Is it for SPC or for Scrap decisions? The "rating" systems are not same for those very different applications. I fuzzy gage often work OK for SPC, but not for scrap decisions (which use spec limits rather than control limits).
below is my question :
if the GR&R result is : %R&R=100(R&R/TV) = 17.88%;
: %R&R=100(R&R/Tol) = 9.53%,
which is the correct result to determine the GR&R study?;
and why?
Gage R&R %Contribution
Source VarComp (of VarComp)
Total Gage R&R 0.0000000 1.20
Repeatability 0.0000000 1.20
Reproducibility 0.0000000 0.00
INSPECTOR 0.0000000 0.00
Part-to-Part 0.0000013 98.80
Total Variation 0.0000013 100.00
Process tolerance = 0.008
Study Var %Study Var %Tolerance
Source StdDev(SD) (6* SD ) (%SV) (SV/Toler)
TotalGR&R 0.0001263 0.0007577 10.95 9.47
Repeatability 0.0001263 0.0007577 10.95 9.47
Reproducibility 0.0000000 0.0000000 0.00 0.00
INSPECTOR 0.0000000 0.0000000 0.00 0.00
Part-to-Part 0.0011467 0.0068801 99.40 86.00
Total Variation 0.0011536 0.0069217 100.00 86.52
Number of Distinct Categories =12
Need to know what percentage I'm I suppose to use to determine weather my GRR Crossed is acceptable?