![]() ![]() Next, we can look graphically at the charts in Minitab for deeper understanding and troubleshooting (if necessary). If you extract these numbers from the analysis tables above, and review it against the criteria, you can see why Tab Width is deemed “BAD” (unacceptable measurement system), and Cap Bow is deemed “GOOD” (acceptable measurement system). If you have below 5, then it needs improvement and would be considered unacceptable. If you have between 5 and 10, that’s considered marginal (still acceptable, but could use improvement). The goal is to have a NCD of 10 or greater. If they fall within the same “bucket” then we cannot tell those items apart from each other. To have more accuracy and precision in our measurements, we want to have many different buckets, which allow us to tell one item from another. Think about these as “buckets” in which your measurement system can group your data. The number of distinct categories also represents the number of groups within your process data that your measurement system can discern. Note: Not every measurement has specification limits, so % Tolerance may not be applicable, but % Study Variation will also be calculated. There are some exceptions to this, depending on how capable the measurement is within the specifications, which we’ll discuss below. We will discuss this criteria in the sections below, but basically you want these numbers to be less than 10% ideally, but no greater than 30%. Total Gage R&R %Study Var and Total Gage R&R %Tolerance Anything between 1 and 9% would be considered marginal. The criteria is to have less than 1% of the variation due to Total Gage R&R, and no more than 9%. ![]() These percentages are related closely to the % for Repeatability and Reproducibility in other tables, but they sum up to 100% (where the other ones do not sum to 100%, which is confusing for many). % Contribution is the percentage of overall variation from each variance component: Repeatability, Reproducibility (Operator and Operator*Samples) and Part-to-Part variation. We look at 4 criteria to determine how good the Gage R&R results are: Here are the main results for the Minitab data analysis, shown as a summary table. Minitab will generate both data analysis tables along with graphs. ![]() 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) 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. You need to monitor and evaluate the effectiveness of your actions using the same or a revised control chart.Minitab provides a great Gage R&R Sixpack (6 sections) report, when performing a measurement systems analysis (MSA) study. You need to implement corrective actions or preventive actions to eliminate or reduce the impact of the special cause of variation on your process. You need to analyze the data and information using root cause analysis tools, such as fishbone diagrams, Pareto charts, or 5 whys. You need to collect data and information about the process conditions, inputs, outputs, and environment that may have influenced the signal. To interpret and act on a signal, you need to investigate the source and the nature of the special cause of variation that generated it. A signal may also be temporary or permanent, depending on whether it is caused by a transient or a persistent factor. A signal may be positive or negative, depending on whether it improves or worsens your process output. A signal indicates that there is a change or a disturbance in your process that is not due to common causes of variation. You can apply these rules and tests to any type of control chart, regardless of the distribution of your data, as long as you use the appropriate control limits and probabilities.įinally, when you apply control chart rules and tests to your non-normal data, you need to interpret and act on the signals that you find. Some examples of control chart rules and tests are the Western Electric rules, the Nelson rules, the runs test, or the cusum test. There are different sets of control chart rules and tests that use different criteria to identify signals, such as the number, position, or trend of points within or outside the control limits. The shorter the run length, the more likely there is a special cause of variation. The longer the run length, the more stable the process is. Control chart rules and tests are based on the concept of run length, which is the number of consecutive points that fall within the control limits before a point falls outside. Once you have chosen a control chart for your non-normal data, you need to apply control chart rules and tests to detect any signals of special causes of variation in your process. ![]()
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