#Type 1 gage study minitab how to#

The next chart (top right) shows the individual readings of each part. But you could tell the difference between 7.8 and 9,2, because they are outside the limits Since they are within the limits, you cannot tell the difference between the 8.3 and 8.8 due to variation in the measurement system that is too large. For example, if the limits on that chart are between 8 and 9, and you have two measurements: 8.3 and 8.8. We want to be able to tell all items apart from each other. If the chart shows data points within control, then your measurement system is making it difficult to measure part to part differences.Īny measurements that happen to fall inside the limits should be assumed that you cannot distinguish it from random measurement error, which is not good for our system.

This means that the part variation is easy to detect, despite the variation in the measurement system. If you have a good measurement system, then this chart should be out of control (all points outside the control limits). The X-bar chart (bottom left) shows the part variation, using the measurement system R chart from above, for the limits. Any data points outside the limits might highlight some special cause, in that one operator or equipment could not consistently measure that item on the 2nd or 3rd time, and it might be due to a mistake or error or missed step, instead of just natural variation in the measurements. The control limits provide a typical spread of how much measurement variation is expected, and so any data points within the control limits means that it fell within the typical measurement spread. In the R chart, you are measuring the Gage R&R variation from the operator/equipment, so it is calculated based on how much variation you have, relative to the difference in repeat measurements for each operator/equipment. If it is out of control (points outside control limits), then there is no consistency to the measurement system. If your R chart is in control (almost all data points inside the control limits), then that is a good sign. The next chart, the R chart (middle left), shows the repeatability and reproducibility variation. This will be evident on the other charts as well, so make sure your conclusions match all the charts. In the bad example, most of the variation is coming from Reproducibility (Operator), rather than Repeatability. If the system is unacceptable, look at the ‘Repeat’ and ‘Reprod’ groups, to see where the majority of the variation is coming from. UNACCEPTABLE – Must improve measurement process so % of Tolerance is less than 10%, or less than 30% with Ppk greater than 1.67 MARGINAL – If Ppk is less than 1.67, then improve measurement process until % of Tolerance is less than 10%, otherwise, no action is required Here is the criteria for determining if your measurement system is adequate, using the different % calculations in Minitab (taken from AIAG guidelines for the gage R&R table % of Tolerance Result The good chart below that shows a very small blue bar, which means the measurement system takes up very little variation, compared to the spec limits of the item being measured. In the examples above, the far left group of charts shows a blue bar above the 50% mark. If the % is less than 10%, then I have an adequate measurement system. I would then look at whether the repeatability or reproducibility % was greater, in order to determine what I need to improve.

If that number is greater than 30%, then I have a problem with my system. Others use ‘% of StudyVar’ (green) or ‘% of Contribution’ (orange) This provides me with a quick assessment of the measurement system. The first place I look is the R&R variation as a % of Tolerance (blue). There are three comparisons made from these results: % of overall variation (StudyVar), % of Tolerance (Spec Limit width, if entered into report), and % of Contribution. The first chart (upper left corner) is your breakdown of variation, into Part, Repeatability, and Reproducibility. In this article, we will look at two different examples, one for measuring TAB WIDTH (poor results), and the other measuring CAP BOW (good results)

#Type 1 gage study minitab how to#

However, there is some confusion and a lack of knowledge on how to interpret each chart, in order to better understand the validity of your measurement system. Minitab provides a great Gage R&R six-pack chart, when performing a Gage study.