MSA / Gage Capability (GR&R)

1. MSA / Gage Capability (GR&R)

2. Cause & Effect Diagram for a Measurement Process

3. Properties of Measurement Processes • Repeated measurements will disagree • Means of repeated measurements will disagree • Measurements made at different times, or by different operators, • or on different instruments will disagree • The measured value and the true value will disagree Waste due to poor quality test data • Rejection of “good” material • Acceptance of “bad” material • Adjusting the process when not needed • Failure to adjust when needed • Loss of “goodwill” between production and test people

4. The High Cost of Poor Quality Measurements • Materials, lost time, wasted effort • Lower capacity and productivity • Higher manufacturing costs • Lower outgoing quality levels • Late delivery • Unhappy customers • Loss of customers?? In any program of control we must start with observed data. Of what value is the theory of control, i.e. SQC, if the observed data going into it is bad? Dr. Walter A. Shewhart 1931

5. Gage Capability Gage Capability is a method to determine how much of your observed process variation is due to measurement system variation. A Gage Capability Study will break down the total variation into two categories, part-to-part variation and measurement system variation. Measurement system variation is then partitioned into its two components –Repeatability and Reproducibility Overall Variation Measurement Variation Part-to-Part Variation Reproducibility (Variation due to operators) Repeatability (Variation due to gage) Operator-by-Part Interaction1 Operator 1 – available from the ANOVA method only.

6. Repeatability and Reproducibility Repeatability • The variation obtained when one operator uses the same gage for obtaining • replicate measurements of the identical characteristics on the same parts • Obtained under a limited set of operating conditions (see below) Reproducibility • The variation in the average of measurements made by different operators • using the same gage when measuring identical characteristics of the same parts • Obtained under a broader set of operating conditions (see below)

7. Gage R&R (GR&R) Basics • Select the gage and test characteristic to be evaluated • Select 10 parts for the study • Select 3 operators for the study • Each operator tests all 10 parts and records the measurements • Each operator repeats this step a second time • Each operator repeats this step a third time • Operators do not have access to his/her previous results or the results of others • Tests are done in random order • Data (90 data points) entered into the computer (Recommended to use Minitab) • Tolerance (USL minus LSL) entered into computer • Software prints out information, including: • - GR&R % of Tolerance • - GR&R % of Study Variation • - Other statistical measures and graphs • General requirements: • - GR&R % of Tolerance not more than 25% for key dimensions • - GR&R % of Tolerance not more than 35% for non-key dimensions GR&R % of Tolerance = the percentage of allowable tolerance occupied by test variation (repeatability and reproducibility) alone.

8. Comparison of Various GR&R % of Tolerance LSL USL GR&R = 25% GR&R = 30% GR&R = 40% GR&R = 50% GR&R = 100%

9. Lower Spec Limit Upper Spec Limit Say you test a part and get a result right here. The Impact of GR&R on Specification Limits • Assume: • An in-control, unbiased measurement process • GR&R % of Tolerance = 20% X The test result indicates “in-spec”.

10. Lower Spec Limit Upper Spec Limit Because the GR&R is 20%, you know that the “true” value may lie anywhere in a region this wide. The Impact of GR&R on Specification Limits • Assume: • An in-control, unbiased measurement process • GR&R % of Tolerance = 20% Because of test variability however, you know that the “true” value might be higher or lower than the measured value. X The problem is, where do you place this interval, with respect to your measured value?

11. Lower Spec Limit Upper Spec Limit Do you place it here? Do you place it here? Or do you place it here? The Impact of GR&R on Specification Limits • Assume: • An in-control, unbiased measurement process • GR&R % of Tolerance = 20% X Which is correct?

12. Lower Spec Limit Upper Spec Limit The Impact of GR&R on Specification Limits • Assume: • An in-control, unbiased measurement process • GR&R % of Tolerance = 20% You can see that there is a distinct probability, however small, that an additional test result will indicate “out-of-spec”. X You treat this question as follows: Consider your current test result to be your best estimate of where the mean of this interval lies, and center the interval on the test result. Let’s generalize this issue with an example that uses three different parts.

13. Wrap-up: The Impact of GR&R on Specification Limits Three different parts: 1, 2, & 3 Each part is represented by a distribution representing known measurement variability, rather than an interval. For judging conformance to spec, the GR&R will not be a factor for parts 1 & 3. As in the previous example, the test result for sample #2 presents a distinct probability that the part may actually be out-of-specification, even though the test result is within spec. Whenever a single test result falls directly on a spec limit, there is a 50:50 chance that the part may be in-spec or out-of-spec. This is true regardless of how “good” the GR&R is. For these reasons, it is always best for the process average to be at or near the target value.

14. Case 2B - Unilateral Specs, Have USL or LSL but not both: Tolerance = 2 * X – Spec Limit where, = absolute value, X = process average Determining the “Tolerance” Case 1 - Bilateral Specs: Upper Spec Limit (USL)and Lower Spec Limit (LSL) available Tolerance = USL minus LSL Case 2A - Unilateral Specs, Target = 0; Have USL: Tolerance = USL minus Zero Note: Minitab software uses this method, starting with Release 14.2 Case 3 – No Specs, but have production data: Tolerance = 6 * process standard deviation Results in a GR&R % of Process Capability

17. Two Types of GR&R XBar & R Method and ANOVA Method The calculations used in the XBar and R method are simpler, however the ANOVA method is preferred because 1) it uses standard deviation rather than Range statistics and is more accurate, and 2) provides the statistical significance level for operator-part interaction. If the software offers both methods, use the ANOVA method. The method used should provide an Xbar chart and a Range chart. The R chart shows the difference between the largest and smallest measurement for each part for each operator. Because the points are arranged by operator, you can see how consistent each operator is. Ideally, you would like to see that the average Range is about the same for each operator, and that no points exceed the upper control limit. The Xbar chart shows the average for each part by each operator. Because the points are arranged by operator, you can see how each operator’s averages compare. Unlike a conventional control chart, you want the gage study Xbar chart to show many points exceeding the control limits. If no points exceed the Xbar control limits, it means that the test method cannot distinguish between different parts. The more out-of-control Xbar points, the better.

18. Software Example – Minitab Analysis This is how you arrange the data for Minitab. Use one column for sample number, one for operator number or name, and one for the data values. For 10 samples, 3 operators and 3 replicates, you should have 90 data points. Choose the ANOVA method for your analysis. Choose six standard deviations for the Study Variation. Previous versions of Minitab used 5.15 standard deviations. 6 standard deviations however is more in line with conventional estimates of capability. Input the tolerance. Examples: If specs are +/- 0.031”, the tolerance is 0.062” If specs are –0.005”. +0.025”, tolerance is 0.030” If specs are 0.08” max and a target of 0” is assumed, tolerance is 0.08”.

20. GR&R for Length, Part # 12345

21. Minitab will also show the “Number of Distinct Categories”. This is the number of distinct categories of parts that the measurement process is currently able to distinguish. The lower the %GR&R, the higher this number will be. Ideally you should have 5 or more distinct categories. This example had only 2 distinct categories, but only because the variability of the 10 parts was small when compared to the allowable specifications. If the GR&R % of Tolerance is acceptable and GR&R % of Study Variation is too high, it means that your parts are too uniform to use the % of Study Variation as a reliable measure of gage capability.

22. Example #2 (GR&R = 12.3% of Tolerance)

23. Example #3 (One Distinct Category, GR&R = 114% of Tolerance) Handling Interaction If the ANOVA table shows a statistically significant interaction, it may or may not be real. If you cannot get it to repeat, conclude that it is probably not a true interaction.

24. In-Class Gage R&R Demonstration Parts to be tested: Golf Tees Specs: 0.440 +/- 0.020” Gage to be used: 6-inch Calipers Software: Minitab (ANOVA Method) Perform the GR&R, add the data to Minitab and review results.

25. What do I do if the GR&R fails to meet requirements? • Eliminate obvious causes: • - Poor repeatability for one or two operators – training • - Poor reproducibility among one or more operators – investigate for cause • Significant interaction – investigate; if not confirmed as real and/or repeatable, • disregard • Poor repeatability for all operators – inadequate training is possible, but check • for excessive within-part variability (If %GR&R is poor due to within part variability, don’t blame the gage or the operators – the fault is with the process). • Use the Advanced GR&R Procedure (on the CD) to troubleshoot other aspects of the measurement system, as appropriate: • Gage Run chart • Accuracy (bias from the true value) • Statistical differences between operators (mean and/or variation) • Stability of the measurement process over time • Linearity (mean and/or variation)

26. Questions? Comments?