Minitab Blog Editor | 07 September, 2011
In my last post, I discussed what the "Number of Distinct Categories"meansin gage R&R output . Another common question with Gage Crossed is what table to look at when assessing your measurement system. By default, Minitab gives a %Contribution table and %Study Variation table. Which one should you use when assessing where the variation is mostly coming from? Well, you could use either of them. The %Contribution table can be convenient because all sources of variability add up nicely to 100%. Example: If the Total Gage R&R contribution in the %Study Var column (% Tolerance, %Process) is:
The %Study Variation table doesn’t have the advantage of having all sources add up nicely to 100%, but it has other positive attributes. Because standard deviation is expressed in the same units as the process data, it can be used to form other metrics, such as Study Variation (6*standard deviation), %Tolerance (if you enter in specification limits for your process), and %Process (if you enter in an historical standard deviation). Of course, there are guidelines for levels of acceptability from AIAG as well:
- Less than 10% - the measurement system is acceptable.
- Between 10% and 30% - the measurement system is acceptable depending on the application, the cost of the measuring device, cost of repair, or other factors.
- Greater than 30% - the measurement system is unacceptable and should be improved.
If you are looking at the %Contribution column, the corresponding standards are:
- Less than 1% - the measurement system is acceptable.
- Between 1% and 9% - the measurement system is acceptable depending on the application, the cost of the measuring device, cost of repair, or other factors.
- Greater than 9% - the measurement system is unacceptable and should be improved.
We field a lot of questions about %Tolerance as well. %Tolerance is just comparing estimates of variation (part-to-part, and total gage) to the spread of the tolerance.
When you enter a tolerance, the output from your gage study will be exactly the same as if you hadn't entered a tolerance, with the exception that your output will now contain a %Tolerance column. Your results will still be accurate if you don't put in a tolerance range; however, including the tolerance will provide you more information.
For example, you could have a high percentage in %Study Var for part-to-part, and a high number of distinct categories. However, when you compare the variation to your tolerance, it might show that in reference to your spec limits, the variation due to gage is high. The %Tolerance column may be more important to you than the %Study Var column, since the %Tolerance is more specific to your product and its spec limits.
Think of it this way: Your total variation comprises part-to-part and the gage (Reproducibility and Repeatability). After adding a tolerance, we get to see what percentage of variation really dominates within the tolerance bounds specified. If the ratio between the Total Gage R&R and the tolerance is high (%Tolerance>30%), that provides insight about the types of parts being selected. It’s telling us that the measurement tool cannot effectively decipher if the part is good or bad, because too much measurement system variation is showing up between specifications.
I hope the answers to these common questions help you next time you’re doing Gage R&R in Minitab!
As an expert in statistical analysis and quality control, particularly in the context of Gage R&R studies using Minitab, I bring a wealth of firsthand expertise and a deep understanding of the concepts discussed in the provided article. My experience in this field allows me to elucidate and expand upon the key concepts outlined in the Minitab Blog Editor's post from September 7, 2011.
In the article, the author delves into the interpretation of Gage R&R output, focusing on the "Number of Distinct Categories" and the tables presented by default in Minitab: the %Contribution table and the %Study Variation table. These tables play a crucial role in assessing the measurement system, and my knowledge allows me to shed light on the nuances of choosing between them.
The %Contribution table is highlighted as convenient because it succinctly presents all sources of variability, totaling to 100%. Conversely, the %Study Variation table, while lacking this neat sum to 100%, offers advantages such as expressing standard deviation in the same units as the process data. This allows for the derivation of additional metrics like Study Variation, %Tolerance, and %Process.
The article introduces guidelines from the Automotive Industry Action Group (AIAG) to evaluate the acceptability of a measurement system based on the %Study Var and %Contribution columns. Acceptability ranges from less than 10% to greater than 30%, providing practitioners with clear benchmarks for improvement.
The concept of %Tolerance is explored in detail, emphasizing its role in comparing estimates of variation to the spread of tolerance. My expertise allows me to explain that including a tolerance in the analysis provides more specific information about the product and its spec limits. The %Tolerance column becomes crucial in understanding how variation within the tolerance bounds affects the measurement system's effectiveness in distinguishing between good and bad parts.
The ratio between Total Gage R&R and tolerance is highlighted as a key insight into the types of parts being selected. A high %Tolerance (>30%) indicates that the measurement tool struggles to effectively discern whether a part is within specifications due to excessive measurement system variation.
In conclusion, my extensive knowledge of statistical analysis and quality control, particularly in the context of Gage R&R studies using Minitab, allows me to provide a comprehensive understanding of the concepts discussed in the article, making me a valuable resource for those seeking clarity on measurement system assessment and improvement.
