# Gauge R&R on a torque measurement

#### Jayfaas

##### Registered
I work for a driveshaft manufacturer and we are measuring joint articulation torque on driveshaft universal joints. The tolerance is 0.3Nm to 0.7Nm so already its a torque measurement with a very small window for variability. What we are seeing is that the measurement is climbing from each operator. For example, operator 1 measurements look like .52, .58, .61, so my range is looking high but the next operator is having roughly the same measurements (.53, .57, .62) so each trial across the operators is consistent but because our measurement is climbing, its making our range look big and I believe its what is causing us to fail. I have been running a nested ANOVA and getting failing results. Am I running the wrong study for this test? Any ideas?

#### optomist1

##### A Sea of Statistics
Trusted Information Resource
"In God We Trust, All Others Must Bring Data - Dr. Demming", please share a spreadsheet or similar with data and results....helps move the help process and discussion along...

Here is the data

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#### Bill Levinson

##### Industrial Statistician and Trainer
If I run it in StatGraphics, I get zero reproducibility (appraiser variation) for JAT Rear, but I am using the average and range method and not nested ANOVA. I understand that a nested model is sometimes used for destructive or other non-reproducible testing (but I have never worked with this), but your application looks perfectly amenable to the average and range method, or ordinary ANOVA in which the parts are one factor and the operator is another. We expect a difference between the parts but hope to not get any between the operators.

#### Jayfaas

##### Registered
Im sorry. I was running a crossed R&R study in Minitab. I also used a spreadsheet we have that does average and range method and it was above 20% for both.

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#### Miner

##### Forum Moderator
Staff member
What we are seeing is that the measurement is climbing from each operator. For example, operator 1 measurements look like .52, .58, .61, so my range is looking high but the next operator is having roughly the same measurements (.53, .57, .62) so each trial across the operators is consistent but because our measurement is climbing, its making our range look big and I believe its what is causing us to fail. I have been running a nested ANOVA and getting failing results. Am I running the wrong study for this test? Any ideas?
The pattern you describe is only apparent on parts 5 thru 10 (see multi-vary plot). These parts also have higher torque. Is there something different about these compared to parts 1 thru 4? A crossed R&R study assumes that the parts do not change when measured. Sometimes parts do change when measured (e.g., rubber parts). However, only some of these parts appear to have changed. Parts 1 thru 4 have less variation and no discernible pattern. Before we worry about the correct analysis, I recommend investigating the parts.

#### Jayfaas

##### Registered
We had our guy make them higher for the ANOVA method

#### Miner

##### Forum Moderator
Staff member
So they are not from the normal process? This may have resulted in unintended differences. It is also unnecessary as the parts should reflect normal process variation.

#### Jayfaas

##### Registered
These parts are from our staking process but we had an engineer change some of them to be low, some to be normal, and some to be high so we will have the variance for the ANOVA study. The issue is that its a torque measurement with a very small tolerance window, and because the torque measurement may not necessarily be repeatable, Im wondering if a Gauge R&R is the right test for this.

#### Welshwizard

##### Involved In Discussions
Hi Jayfass, from what you have said the part data has been rearranged for the study. If we select 10 parts and perform repeated measurements on each with multiple operators, then go on to compute a standard deviation representing repeatability it's valid to compare this against the specification width to compute a consumption of tolerance but only if the measurement process is consistent.

However, if we want the result of the study to represent what we see in the production process and not just a measurement repeatability study between operators then we need to select parts in a way which best represents the variation in the production process. For a torque study we have aspects like variation in nuts and bolts, hole, lubrication, washers etc etc to rationally take into consideration

As long as we understand what we are trying accomplish and what the results will portray against what the customer requires.

Front - measurement process is not consistent, particularly affecting parts 6 and 7 across 2 operators, this requires looking at before making any characterisations about the measurement process because the standard deviation is clearly not stable and therefore not representative.

Rear - Measurement is consistent, no detectible operator bias (reproducibility), the real consumption of repeatability at a minimum of 96% confidence for the samples chosen is 12%.

Its true that the average torque values for the parts in both studies appear to climb but as the data has been re arranged into Low Med and High that's hardly surprising. This type of study doesn't care about the patterns of averages or ranges as long as they are not detectibly different.

I hope this helps

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