Hi abichou,
Your points from each of your posts:
You want to be able to analyse, interpret and learn about the R Chart in the context of a Gauge R and R (GRR) Analysis.
- The R (Range) chart plots the maximum minus the minimum differences for the repeated measurements of each part by each operator, in this way it depicts the repeatability and consistency of the measurement process across the studied operators. The Range Chart has additional lines plotted to show the average range of the studied operators and also an Upper Control Limit (UCL)
The UCL filters the everyday random variation from the special cause variation thus if it is breached by a measurement it implies that there is a lack of consistency in the measurement process across your studied operators. The software goes on to characterise the usual numbers but they all rely on the process demonstrating random variation, which in your case it doesnt.
When you find the reason for the breach of the control limits you must put something in place to try to minimise it happening again because the implication is that this measurement process is important to you and you need to aspire to consistent measurements. You will find that if you do seek out and eradicate the special cause that the performance of the measurement process will improve for usually very little effort.
You want to understand why all the operators usually don't measure the same value on the same sample and also there is some sample on the LCL and above the UCL what does all of this means.
- The laws of natural variation and physics will dictate that each operator will on balance record different values for parts, this is to be expected, in fact if this doesn't happen you need to be cautious. The UCL helps you to filter and therefore not worry about the extent of the random variation but rather alert you when anything other than random variation is present, this is the essence of process behaviour charts such as this. Of course, if you only display random variation and the performance of the measurement process is still not what you want then you need to think about how you could improve things.
You have a relatively high P to T value, what does this mean?
- You are referring to the ratio of measurement variation to tolerance expressed as a percentage, this is known as the P/T ratio. You may have pre set ideas in your mind about good or bad values here based upon your learning or mandated by a customer.
The P/T ratio simply uses multiples of the standard deviation and divides it by the width of the tolerance, it implies that the higher the percentage from the ratio the higher the risk is for using the measurement process to classify parts.
What to do next?
- Ensure that the measurement study reflects what would happen in a production situation whilst using this measurement system, otherwise the outcomes mean little, for example:
Do you have a method that should be followed?
Was the study conducted in controlled or real life conditions?
Are the operators trained and familiar?
- Find and formally correct the reasons for the special causes for the operator, think about the impact on other operators who could be used on this task
- Re run the study, witness it and take notes
- If all is ok with consistency publish and ensure the disciplines are cascaded to the organisation, if not correct and re run
- Consider that if the operators are consistent this is the best that you can achieve without either time consuming repetitive measurements or another measurement device and process.
Final Observations
Looking at your reports without access to the raw data from experience I would say that the apparent differences between the operators are in the noise of the measurements therefore they are not there. This means that if you could find and eradicate the reason for the inconsistency for operator 3 measurements you could claim at the minimum the standard deviation for Repeatability if not better.
If this still does not match up to your requirements you best look for a different method.
Hope this helps