Welcome to this post of presentation in the applications of MSA platform tools in JMP 17. Before we go into it, I just would like to give a brief description about Measurement System Analysis, short for MSA. So when we look at the total observed variation in the process, we use measurement system analysis to try to identify and manage the sources of variation that can influence the measurement system being used. It's a combination of measurement devices, people, procedures, standards, etc. So we can decompose that total observed variation into two other components, the process component, or sometimes called part to part variation. But what we're really interested with measurement system analysis is in this measurement system variation component.
Looking at the measurement error that is associated with this measurement system variation, that can be broken down into other components, precision and accuracy. For the purpose of this poster, I will concentrate on the tools that enable us to identify sources of variation within precision and specifically repeatability and a little bit about bias component under the accuracy component.
What we tend to do when looking at measurement system analysis, there are several methods involved, normally and particularly for continuous data. We start by examining the accuracy and consistency of the measurement device alone using a technique called type 1 gage study. This is sometimes also named as analyzing the pure repeatability of the system. So we have one single part, one single device if the measurement system requires a manual intervention, we can have one operator. But the idea here is that we start evaluating the pure repeatability of the system before going into more complex analysis where other sources of variation may be part of the measurement system, which essentially is the second step around what is called Full Gage R&R, which examines both repeatability and reproducibility.
Last but not least, we have continuous gage linearity and bias study, but it's not going to be covered specifically in this poster. So let's have a look of what this means in terms of JMP 17. In the new version of JMP, we do have a new MSA method, type 1 gage study, that essentially is going to help us identify that initial phase of the analysis with regards to the pure repeatability of the system. So I'll show you a quick example of that of an output report with the Type 1 Gage R&R. And what you can see here is that by default, the report shows a run chart. So this looks at 30 repeats of the same part using the same device or equipment in order, so this timeline really helps us identify any special situation, any special measurements that didn't work very well.
There is a reference that we're on the nominal. If you're using a reference part, for example, we can definitely identify whether the average of those measurements are in line with the reference part. That mean value can be added to the graph if we want to. As you can see, it's going to be on top of it. But if I remove the reference line, then you can see average and the reference are very similar for this example.
We also want to have a look at this Type 1 Gage R&R study and have a reference around 20 % of our tolerance. What in the type 1 gage study we're doing, we are limiting the analysis to only 20 % of the total tolerance in order to assess whether the pure repeatability is acceptable or not. But this specification, if you will, for the type 1 can be all consented in the settings of this tool.
It provides some summary and capability statistics, so the normal reference location and spread references, particularly when it comes to six standard deviations, the number of measurements taken and the tolerance. T hen here are the two limits above reference on the graph for the 20 % of the tolerance, so plus or minus 10 % of that tolerance.
If you use to the process capability indexes, what you will see now for capability of the gage, CG and CGK, they are exactly the same. The biggest difference here is that obviously we're looking at the capability of the gage and assessing this variation with regards to, in this case, the 20 % of the tolerance, but suddenly we can have a look at both the variation relative to those spec limits and also variation and location for CGK. So this gives us a summary of some metrics to evaluate the Type 1 Gage R&R study results. There are some percentage being calculated as well in terms of percentage of variation with regards to repeatability, but obviously, if you're using a reference part that you already know its nominal value, we can evaluate not only the pure repeatability of the system in this case, but also the bias. So the difference between the reference value and the nominal value.
So that can be actually added as an additional test if we want to, so this is really looking at the hypothesis testing case in terms of if the bias is equal to zero so either the average and the reference value are the same or very close to each other, and as you can see, the reported P value there, in this case, statistically, there is no significant difference between average and reference value.
Another useful visualization within this tool is the history realm, so we can have a look at the distribution of the values of those measurements taken, in this case, those 30 measurements for reference, that can be customized in your report as we go along. So very quickly, we just have a great tool to initialize our measurement system analysis process by now having what is called a Type 1 Gage R&R Study as part of the MSA platform in JMP.
What we sometimes do is we also have a second step before we go into what is called a Full Gage R&R assessment, both repeatability and [inaudible 00:07:43] , which can be called a Type 2 Study. We have an example here of that, so the only difference here is the fact that in between the 30 measurements, we've removed the part from a potential holding fixture in between each measurement. As you would expect, by doing that intermediate step in between all the measurements, then we expect to have more variation, and this is what we can see here now. So not only we have more variation, where you can also see that the average value of the readings are also much lower than its target location.
By turning on the bias test, we will be able to see that now the P value when compared to the Type 1 study where we didn't remove the part in between measurements, the part was fixed and just measured 30 times consecutively, we now have a low P value showing that there is significant difference between the reference value and the average. T his can be built into several increment and sources of variation even before we start adding multiple parts and operators or equipment into this analysis.
But if we do, JMP already had a gage on our study tool in previous versions. This is just a quick example, what that means in terms of variability of the gage, a nd in this case, we use the gage on our method involved. So if you go to Analyze, Quality and Process, there's an updated version for the Measurement System Analysis. In the MSA method, we can see now we have the Type 1 Gage study that I've used for both Type 1 and Type 2. The only difference there is that on the report, the output report for the Type 2, I just edited the title and called it Type 2 just to differentiate between the two output reports. But what we're seeing here for the Full Gage R&R or the variability analysis, I've used the Gage R&R method, and this is where we can also decide the type of model used, normally used as cross, so we can see all the effects crossed with each other in the analysis, as well as some additional options are also available.
But in this report, I'm customizing, in this case, I've added some specification limits. Here, essentially, we're not looking at the reproducibility of the system. It's just another increment we evaluate repeatability in this case. We are using not one, but 10 parts in this case and evaluating that variation for five repeats of each part. As you can see, in the Gage R&R report and table, the reproducibility component is zero because we don't have additional equipment or operators being evaluated in this analysis, so all the variation in this study is due to repeatability. So this is the traditional output that you would get from the Gage R&R method inside of JMP for reference.
To finalize the new tools involved in JMP 17 for the MSA platform, what I would like to highlight as well is that as part of the planning phase for any Gage R&R study, it's important to understand what is the method utilized, of course, but also what will be a good method of data collection. A s part of JMP, we now have as part of DOE special purpose, a new tool called MSA Design, and this enables adding factors like parts and operators. If I quickly show adding three factors there, I can identify what is the MSA role involved in each one of them, for example. This is a great opportunity during the planning phase to start to come up with the design that will help you doing the data collection even before any analysis is done.
For more information about how to utilize the MSA design feature, you can follow this link, which will take you to the JMP user community video where Hyde Miller, JMP systems engineer, has provided more information about this tool.
Hope this was useful for you. Thank you very much.
