Performing Gage R&R on Automated Thickness Measuring Machine
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This topic contains 5 replies, has 4 voices, and was last updated by 
Chuck White 2 months, 1 week ago.
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- October 3, 2018 at 7:53 am #56107
Hello all,
I have a machine that is used to measure the thickness of flat pieces across the width and length. There are 5 thickness gages across the width (set up by an operator for the different widths of parts), and the system automatically collects a thickness data point, for each gage, every 6 inches down the length of the part.
We want to look at the accuracy and repeatability of this system.
My question is, should I perform a Gage R&R for each thickness sensor? Also, since I need a single data point to compare against, should I use an average of the collected data points, or should I use a common data point from piece-to-piece (e.g. 12 inches from the first edge for every part).
Thanks,
JoeOctober 3, 2018 at 9:27 am #203091Yes, since it sounds like the measurement is measuring up to 5 items across the width–do gage R&R across all separately.
October 3, 2018 at 10:39 am #203092@cseider Just a thought. If I do them all at the same time and the gage is acceptable I shouldn’t need to do it separately? It might not be a perfect answer but it would be faster and less work.
If I have 5 gage readings every 6 inches I can see an opportunity for some simple hypothesis testing as well.
October 3, 2018 at 1:45 pm #203093Oh, I just meant to do the analysis of each separately–of course, I’d “gather” and do the physical gathering, etc at once. Glad you clarified to Joe K.
October 3, 2018 at 2:58 pm #203096Thanks, guys!
Follow-up question: If I wanted to do a gage R&R for each sensor, how should I compare part-to-part? Should I average the thickness data points from the sensor and use that to compare between parts (for part variability)?
October 4, 2018 at 7:56 am #207038I recommend choosing a common data point along the length. If you use an average, your study will underestimate the part to part variation, since the variance of sample averages is always less than the variance of individual measurements.
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