No reproducibility Error in Gage R&R?

#1
What could cause having 0% reproducibility error in a Gage R&R study? I had two operators measure parts with 3 repetitions. The repeatability error is 38% (still too high), but I don't understand why there is no reproducibility error. The specification is to the thousandth of an inch and the measurement tool can measure to the tenth of a thousandth. Any help would be much appreciated as I need to explain this in my report. I did repeat this with different operators (the original were not available), and got a combined error of 12%.
 
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#3
See results below. I have attached the original data.

Gage R&R Study - ANOVA Method

Two-Way ANOVA Table With Interaction

Source DF SS MS F P
Part 9 0.0004002 0.0000445 17.8723 0.000
Operator 1 0.0000005 0.0000005 0.2101 0.658
Part * Operator 9 0.0000224 0.0000025 0.9120 0.525
Repeatability 40 0.0001091 0.0000027
Total 59 0.0005323


Alpha to remove interaction term = 0.25


Two-Way ANOVA Table Without Interaction

Source DF SS MS F P
Part 9 0.0004002 0.0000445 16.5673 0.000
Operator 1 0.0000005 0.0000005 0.1947 0.661
Repeatability 49 0.0001315 0.0000027
Total 59 0.0005323


Gage R&R

%Contribution
Source VarComp (of VarComp)
Total Gage R&R 0.0000027 27.82
Repeatability 0.0000027 27.82
Reproducibility 0.0000000 0.00
Operator 0.0000000 0.00
Part-To-Part 0.0000070 72.18
Total Variation 0.0000096 100.00


Process tolerance = 0.026


Study Var %Study Var %Tolerance
Source StdDev (SD) (6 * SD) (%SV) (SV/Toler)
Total Gage R&R 0.0016384 0.0098302 52.74 37.81
Repeatability 0.0016384 0.0098302 52.74 37.81
Reproducibility 0.0000000 0.0000000 0.00 0.00
Operator 0.0000000 0.0000000 0.00 0.00
Part-To-Part 0.0026390 0.0158340 84.96 60.90
Total Variation 0.0031062 0.0186373 100.00 71.68


Number of Distinct Categories = 2
 

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Miner

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#4
The Reproducibility is not exactly zero. It is just small enough that it rounds to zero. Michele averaged 0.288223 while Nathaniel averaged 0.28841. The bias was 0.000187, which rounded to zero. This can happen, and is a good thing when it does.

Of greater interest to me is the dramatic difference in repeatability between the two. Nathaniel has half the variation.

Repeat.png
 

Welshwizard

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#5
Hope i'm not too late for this.

The purpose of analysis is insight so lets explore your data set a bit more.

Sure enough, the range chart as shown by Miner above reveals a difference between the two operators, it also reveals that Michelle's measurements are not consistent therefore any data used from these measurements to summarise any statistics are based on a weak foundation. On the other hand, Nathaniel's measurements are reasonably consistent and therefore there is no reason why anybody else following a similar procedure/technique cannot achieve the same.

We could go ahead and summarise what the statsitics would show for Nathaniels measurement alone and if this was acceptable we could look at why Michelles are detectibly different and fix the process. This process should follow for all other operators who would be destined to use this measurement process.

A glance at the probable error reveals that the fourth decimal place is pure noise and is adding nothing to the measurements so based upon the output of this study the measurements should be recorded to 3 decimal places.

As for the Reproducibility question, its clear that there are no detecible differences between these two operators for bias therefore we dont need to worry about this nuisance aspect.

Based upon the parts chosen for this study more than 80 percent of the variation is due to part variation whilst the rest is measurement variation. This will only get better (less measurement variation) when Michelle's measurement process is fixed.

I have used Don Wheeler's EMP methods for more than 20 years, I attach a file here for your reference. You will not get the insight that I have talked about here with the AIAG method.

Hope you find this usefull.
 

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