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Correlating Gages

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  • #49137

    ncwalker
    Participant

    I have heard of several methods to correlate gages. For each method, I have some questions and then the general question of which one should I use.1) Isoplot – Measure 20 pcs representative of production range on two gages. Plot these results on an X and Y graph and look for alignment to a 45 degree line. That makes sense, BUT to what degree do you say it is good? If I do a linear regression, I would be looking for a slope of 1 and I would get an R. What is the accepable R? What if the R is really good (say .96) but the SLOPE is not 1? If the slope was 2.5 with a good R, I think I could mathematically adjust safely.2) Gage R&R Method – Run a Gage R&R but use each gage as an appraiser. This makes sense to me. I would assume the same Gage R&R hurdles would be used. But if I were doing this on a CMM, would I not have to do this on EVERY dimension that it output? Or just the ones that don’t agree? Or representative dimensions? (A couple of TPs, a couple of Diameters, etc…)3) Percent Difference Method – Measure a couple of parts (3??) on both gages and look at the percent difference between the results. This is a little nebulous. Percent difference of what? The tolerance? How close is good? This is appealing because you are not measureing so many parts, but it just doesn’t feel statistically accurate for the same reason. But we aren’t dealing with statistics, really, are we? I don’t need statistics to tell me that “This one is higher than that one …”4) One Fifth Tolerance Rule of Thumb. Same as above, but the agreement hurdle is that no measurement is different by more than one-fifth the total tolerance.Any help would be appreciated. I need two answers: 1) The right answer. 2) The answer that will make my customer’s SQE happy.ncwalker

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    #167369

    Mike Carnell
    Participant

    ncwalker,
    When you are evaluating gage correlation what you want to know is how well do multiple gages make the same measurement. That is reproducibility and that is simple enough to do with a R&R study just as you described.
    Good luck

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    #167388

    Ward
    Participant

    Mike,
    I sent you an e-mail requesting your current mailing address.  I sent something your way (Harbor Way) and it was returned undeliverable.
    Pete

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    #167389

    Mike Carnell
    Participant

    Pete,
    Will send you the current address. I went back to the Hill Country.
    Regards

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    #167393

    Krishnam
    Participant

    Hi
    ncwalker
    can you send me data on the following email and i will try to solove the same at earlist.
    [email protected]
    Regards,
    Krishnam

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    #167397

    Craig
    Participant

    Sounds like you are trying to answer a vague question. It’s  like going into a pizza shop and just ordering a plain-ole pizza and then yelling because you didn’t get your mushrooms.
    You can match the performance of 2 measurement tools with paired t-testing. (choice 3). Just my 2 cents, but that would be my first choice. Sample size depends on the difference you’d like to detect. Sounds like an easy question, but you’d be surprised how many times I’ve been looked at with a blank stare on that one.
    R&R with machine substituted for operator is also a suitable choice but you lose visibility of the operator effects.
    You could realy turn this into a science project, so I would start out simple.
    HACL

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    #167398

    Craig
    Participant

    I should have proofed my message first! Paired t-testing was not your 3rd option listed. You referenced percent difference.
    My suggestion still holds, but measure a selection of parts on both machines and analyze with paired t-testing. (Not % difference)
    Try hard to explore the preferred method with the customer before you embark on the study.

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    #167406

    ncwalker
    Participant

    Krishnam,I don’t have any data yet. I am trying to define the test so I don’t waste time taking data I cannot use.ncw

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    #167407

    ncwalker
    Participant

    hacl,Let me see if I have this right….The paired t-test is for small sample sizes (because if I had a lot of them, I could use the tails of the normal distribution). I am going to measure parts on one CMM and measure the same parts on another CMM (Once?) maintaining traceability of sample numbers.Then, I line these up and calculate the differences for each part.I get my test value (T) from: avg of differences / variance of differences / sqrt(n)Then I use n and my confidence to look up my t-hurdle statistic (t). (Which is why you say the number of measurements drives how CLOSE I want to know).If T >= t, it means the measurements are different. If T= t, it means the measurements are different. If T= t, it means the measurements are different. If T

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    #167411

    ncwalker
    Participant

    Correction:Something funky happened with my greater thans and less thans,Here is the decision in words:If T is greater than or equal to t, it means the measurements are different and my CMMs do not correlate.If T is less than t it means the measurements are the same and my CMMs do correlate.

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    #167469

    Dennis Craggs
    Participant

    GR&R can be used with appraiser replaced by the gage, but then variation due to appraiser variation is lost. Traditional GR&R compares appraiser, part, and repeat measures. If you want to include more variables, consider using DOE with covariates. Covariates are study variables that are difficult to control, such as individual part values. DOE with covariate analysis basically considers the covariates to have a linear effect on the result. For a gage, you want to assess bias, accuracy, and repeatability over a range of values. Parts could be selected that span this range. Then, two or more gages can be compared as fixed or random factors. In Minitab, Stats -> ANOVA -> General Linear Model provides analysis capability. The statistical significance of any gage difference should be presented in the ANOVA table. The mean difference between individual gages can come from the analysis. Again, the experiment needs to be designed in a balanced manner and there is a linearity assumption for the covariates.

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