Destructive Testing amp; Measurement System Analysis

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    A customer has asked for a “Gage R&R” on a measurement system that degrades the characteristic being measured. Can anyone give me a reference or two on how to handle this, specially anything related to the analysis of measurement systems used in destructive testing.

    I have Wheeler’s book.



    Ken K.

    The only thing I’ve seen that makes sense to me is:

    1. Try to assess the gage independent of the object being tested. For example, if you’re doing a test where one object is pulled from another, and the force of the pull is captured in pounds, you might be able to hang calibrated weights onto the gage and assess the measurement reading. It takes quite a bit of knowledge of the gage and the possible implications/errors that might arrise from the activity.

    2. Create “pseudoreplicates”. Find parts that are as close to identical as possible. Maybe use parts from the same batch or that were manufactured at the very same time. This obviously has its limitations.

    You’ll use the pseudoreplicates to estimate repeatability.

    Since even psudoreplicates can only be measured one time, you can’t estimate operator variation (reproducibility) the usual way (each operator measure each part). You’ll need to run this as a nested (or hierachical) design instead of the usual crossed design. MINITAB has a procedure for this now that should help.

    If you’re not familiar with nested designs I might suggest you pick up a copy of Douglas Montgomery’s “Design and Analysis of Experiments” book. It is well worth the price. He’s an excellent author (I like his writting style).

    If you don’t already have a good general statistics textbook I would suggest his “Applied Statistics and Probability for Engineers”. It covers a wide range of topics except, unfortunately, measurement system analysis. For that I recommend the Automotive Industry Action Group’s MSA Reference Manual. You can find it at

    If you don’t already have them I also strongly recommend their SPC and FMEA references.

    Ken K.


    Jeff Ayland

    Hi Ben,

    I know how you feel on this one, i have a requirement to do an R&R on a specific process.

    The process involves testing switches that are superconducting, this involves running incresing levels of current through the switch. Trouble is, there is a physical phenomenon that takes place as you increase the current that you flow through the switch.

    This phenomenon causes the switch to “train” upwards as it is subsequently tested. Hence you cant test the “same” switch twice, because the physical properties of the switch alter!!

    Also the switch is tested at minus 270 deg C, only a few degrees away from absolute zero temperature. Furthermore, the wire that the switch is made from does not come from nice neat production runs, so testing product that is “virtually” identical is also not possible, or accurate.

    To get a measure of the component of measurement error for retests is nay impossible. Its a real headache.

    Currently we have no way of undertaking DMAIC activity with confidence as i dont know how many switches really are “bad”.




    Grant Blair

    Take a look at “Fundamental Concepts in the Design of Experiments” by Charles R. Hicks.ISBN 0-03-061706-5
    Sounds like you may need a split-plot design.
    However, my best advice, if you don’t mind spending $$$
    is to contact me off-line and I will give you the name of a friend who lives near Clemson, SC. Laboratory SPC is one of his specialties.



    The minitab software has a nested gage R&R for use in analizing destructive test methods.




    Tommy is correct about Minitab, but with the proviso that the parts or units selected for measure are all from a single batch or lot. The closer in time they are selected the less unit to unit variation will get into your analysis. There are other ways to evaluate measurement systems than to use a standard Gauge R&R approach. An alternate way is to perform a sensitivity analysis on the measurement system. Such an analysis allows one to determine if the measurement system will be able to detect differences within a given range of importance, like specifications.

    Let me know if you have interest in additional info on this approach. If you have the Minitab software, Gage R & R studies are referenced in section 11.




    Yes, in minitab we should use the nested option. In minitab, we can only conduct a test with more than one operator. What if (as we are a small business and there is only one operator conducting the tests)there is only one operator?


    Ken K.

    Even if you only have one operator (usually) you’ll still want to understand the typical variability between operators, unless, of course, your current operator is going to be forced to do that same job FOREVER.

    If you still want to use MINITAB with just ONE operator you can use the General Linear Model tool to do the analysis.

    Enter the column containing the measurments in the Response field, and enter the column identifying the different parts into the BOTH the Model and Random factors fields. Make sure you click on the Results button and select the “Display expected mean squares…” option.

    At the bottom of the output you’ll see “Variance Components” listed for the parts column and for “Error”. The latter item relates to repeatability. These values are variances.

    The Total variance is calculated as

    TotalVar=PartVar + ErrorVar

    The Repeatability and R&R variance is equal to the ErrorVar since there is no reproducability, nor is there the interaction between operators and parts.

    Take the square root of each of these to get the respective Total, Part, and Repeatability (R&R) standard devations.

    Multiply each of the stanard deviations by 5.15 to get TV, PV, and EV, respectively.

    Obtain the %Study Variations by dividing PV and EV by TV. Multiply by 100 to get a % value. The resulting 100*EV/TV is the %R&R value of interest to most. This value should be less than 10%

    If you divide each of the variances by the respective TotalVar you’ll get the % Contribution that MINITAB outputs. They are 100*PartVar/TotalVar and 100*ErrorVar/TotalVar.

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