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Informational Use of Classical SPC in Precision Machining

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#1
It is a common misconception in Six Sigma that SPC only works on Normal Distributions. The original work by Dr. Shewhart in the 1930's, and as more recently published by Dr. Don Wheeler provides proofs that SPC is distribution free. So don't worry so much about the distribution of the data, plot the dots and evaluate using X-moving range or Xbar-R.
So what's your take on the application of classical SPC tools on data with saw-tooth distributions (ie: turning / lathe processes with operators making tool offsets to compensate for tool wear as apposed to automated tool wear compensation )?
 
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#2
Shewhart was always practical over statistical. He chose 3s limits empirically to balance the risk of not reacting to the expense of chasing false alarms. I think he would be very accepting of Bob Doering's approach to SPC.
 

Bev D

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#3
And my advice is that further discussion on the sawtooth or systemic cause processes belongs in a separate thread. We should leave this thread to the OP’s question.
 

Steve Prevette

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#5
There was a member here on Elsmar that is (was) very passionate about that discussion and had methods for application of tool wear. You may want to search through the Cove for his posts, drawing a blank on his name, sorry.

Personally, my take would be to plot the first derivative of the data and see if the rate of tool wear was within control, and you could use the UCL of the tool wear rate to detect when to change out the tool in advance of it causing problems against specification.
 

bobdoering

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#7
Both Wheeler and Shewhart showed that Shewhart charting is not limited to the normal distribution. However, they ARE limited to processes with inherently random output distributions. They do NOT work with outputs that are non-random, that is outputs that change as a function over time. In fact, I have shown that they give the EXACT OPPOSITE SIGNAL of the true condition of the process - and the signal is the entire point of the exercise!
 

bobdoering

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#8
Personally, my take would be to plot the first derivative of the data and see if the rate of tool wear was within control, and you could use the UCL of the tool wear rate to detect when to change out the tool in advance of it causing problems against specification.
I find it is more valuable to plot the data as-is, and use it to make key decisions about the process to enable control: when to make an offset, and how much of an offset to make.

The range - depending on how much tolerance you have - can tell you when to change a tool prior to failure by watching the roundness start to increase rapidly. In some micron-tolerance processes, you can actually see when the coating of a tool breaks off! I believe that information is more valuable than variation of the rate itself.
 
P

patricks

#9
So what's your take on the application of classical SPC tools on data with saw-tooth distributions (ie: turning / lathe processes with operators making tool offsets to compensate for tool wear as apposed to automated tool wear compensation )?
At my very first job, we had a shop-floor full of CNC's (80-100) and a few broaching machines. Coming fresh from school, I had no idea that the broaching process had an inherently non-normal distribution (saw tooth). So when I analysed our SPC data, I got pretty excited as I believed this was my opportunity to shine and "fix" the broaching process with the goal of making it follow normal distribution. Luckily, a more experienced colleague quietly explained to me that not all manufacturing process have to follow normal distribution. There was nothing to "fix".

Can you still apply SPC to a saw tooth distribution? Of course. As long as your process follows ANY distribution (normal, saw-tooth, log etc.), you would be able to calculate its standard deviation (i.e. quantify risk). As long as you are able to calculate the standard deviation, you will be able to calculate the control limits. And as long as you are able to calculate the control limits, you can implement SPC. The million dollar question is what is the reaction plan? If there is no meaningful reaction plan to the process deviating from ideal (beyond control limits), all the math is meaningless. This holds true for a process with any distribution model.
 

bobdoering

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#10
Remember - "classical" SPC (Shewhart charts) do NOT work with the sawtooth chart. Yes, you can do SPC, just not Shewhart charts. Shewhart charts require random outputs and the sawtooth curve is a function, not random. Sure, you can calculate the standard deviation, but the charts yield exactly the opposite signal of the true state of the process.
 
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