Using R-Chart to justify removal of part from the study

D

davsnotn

#1
I recently performed a GR&R. All of my data on the R-chart is in control except for one point from one of my operators. Am I justified to remove that part from the study based on the fact that there is less than a 1% probability that it belongs with the rest of the data?
 
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Jen Kirley

Quality and Auditing Expert
Staff member
Admin
#2
Re: Using R-Chart to justify removal of part

No, I would remove the part based on its individual measurement and not the range of the sample.

Are you, at this point able to identify the parts and the measurements that belong to them? If so, good. If not, and if you suspect one was out of tolerance maybe the sample should be remeasured.
 
D

davsnotn

#3
I am at the point where I can identify the parts and the measurements that belong to them. How does one justify removing or repeating a part within a GR&R study. I suspect it is an outlier because it stands out from the rest of the data. However, I also want to be careful I do not manipulate the study. Any advice?
 

Jen Kirley

Quality and Auditing Expert
Staff member
Admin
#4
The GR&R study is about the capability of the measurement equipment and people doing the measurements. So I think I misunderstood your original question and am referring to my MSA Manual by AIAG.

Is the part out of tolerance?
 
D

davsnotn

#5
I am doing a GR&R study with 10 parts, 2 operators, 3 replicates. After I process my GR&R data through MiniTab, the R-chart shows part 1 being out of control for operator A. Operator B is just within control for part 1. When I review my part*operator interaction chart, both operators lie on top of one another, except for part 1. I suspect the data for part one is an outlier and I want to replace part 1 with a different part. My quandry is not having any rules or guidelines for repeating/removing a part from a GR&R study.
 

Jen Kirley

Quality and Auditing Expert
Staff member
Admin
#6
I'm in over my head here, and it's important to recognize that before I lead you seriously astray.

Calling Miner! Bev D can also help, also Statistical Steven and Steve Prevette.

But in the meanwhile I will refer you to Miner's MSA Blog - I saw topics in there about part-to-part variation.

I hope this helps!
 

Bev D

Heretical Statistician
Staff member
Super Moderator
#8
OK in general the Range chart is intended to identify when an unexpected difference happened in one of the repeats. IF you have only one reading O-O-C I recommend REPEATING the measurement NOT removing it. 'outliers' detection isn't intended to result in automatic censoring of the outlier value - it is intended to signal to you that the value warrants investigation. Sometimes we learn far more from the outliers than from the other data points. So WHY was the value out of limits?

If you post your raw data (not minitab output) e may be abel to help you with more specific advice.

For the future: the difficulty with the standard AIAG method is there are so few parts. a better approach is 30 parts and 2 repeats. same number of measurements but statistically much more robust and informative.

and Miner may have even better advice!
 

Miner

Forum Moderator
Staff member
Admin
#9
This scenario usually occurs when there is some variation in form that may or may not be detected by differences in the measurement technique.

An example: Measuring diameter of a cylinder. 1 of 10 cylinders has a burr on the end. 1 Operator measures diameter at the end of the cylinder while the other(s) measure mid-length. The first operator will sometimes measure a diameter that was influenced by the burr, but not always. The other operators will not.

Do not throw out the outlier until you investigate what caused the outlier and what difference existed in the measurement methods that allowed it to influence the results. Your action will depend on whether the cause of the outlier is critical to your process. If it is, adjust the measurement technique to detect it. If it is not, adjust the technique to ignore it.
 
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