Pre-Control Chart to guide in-process inspection / adjustment decisions

D

D.Scott

#31
Dragonair

Your English is a whole lot better than my Chinese. You just keep right on posting.

Once again - Welcome

Dave
 
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D

Dan Kenkel

#32
First of all, this is my first post on the Elsmar Cove forum. I hope this is not too wordy, I am probably breaking an etiquete rule.:)

Secondly, I am new to this tool called "pre-control." I come from the Deming school of quality. So, when you first look at it, it sounds like blasphemy. But, a co-worker engineer and I are currently working on implementing some "pre-control" tools, and I plan to post the results of this effort.

What about this debate?

I think that this is a fascinating debate about which is the better QC tool, "control charts" vs. "pre-control." I must say that Keki Bhote in "World Class Quality," chapters 20 to 22, makes an excellent case against the "traditional control chart" method and for the "pre-control" method. Bhote uses many examples to demonstrate the differences and advantages. He even takes actual examples and shows the data, probability and pitfalls where an operator using a control chart may think that the process is doing just fine and "in-control" when in fact he or she is making defectives with a poor Cpk of 0.51.

I tend to agree with Atul, the proof is in the experiment. So, I will need to perform some parallel process data collections with actual quality data transformed into both tools to examine and compare the two tools, take a look at the line stoppage and corrective action/fix cycles, etc.

Some key things that makes this debate intriguing:

The Deming group would argue that the "pre-control" method is not a clear view of the process variation and it promotes tampering with the system, making rules that the operator must "stop" to make an adjustment--tamper with the system. Deming would say that it invites tampering and does not at all equip the process operator with a tool that lets them continously control and reduce the process variation.

The Shainin-Bhote group would argue that "pre-control" method has enormous power in its simplicity and takes into account the product specification always. And, when the process is already established with a Cpk of 1.33 or better, than the b-risk of accepting bad product--drops to zero.

So which appears to be the better tool?

After reading this thread, and some of the literature behind this debate, I have concluded, for now...

A "traditional control chart" such as the Xbar & Rchart alone does not cut the mustard. If you want to use a traditional control chart, you would need to couple that tool with a running process capability curve-histogram and running Cpk values--a Cpk value that must be maintained daily with a set group of sample data, updating subgroup data regularly. This is not an easy tool to teach to the average machine operator with little to no statistical knowledge. Essentially, you would need to detect, stop and take action to fix that imaginary red-shaded part of the curve that goes over the upper spec. limit. Some software packages have this pictoral tool (curve & histogram) adjacent to the control chart.

The "pre-control" tool appears to be very simple in its "stop" and "go" rules and makes it simple for an operator to understand and follow. Five to six greens in a row assures a minimum Cpk of 1.33, for example.

In the coming weeks I plan to do some actual comparison tests to experiment and prove or disprove my thinking...this will take some time as we need to develop some software or find some that has the "pre-control" tool included. If anyone knows of a software package, let me know.
 

Mike S.

Happy to be Alive
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#33
Wow! Someone willing to do a test! Cool! :applause:

Didn't someone once say a test is worth a bunch of expert opinions? Nah...probably just some nut...:notme:
 

bobdoering

Stop X-bar/R Madness!!
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#34
In the coming weeks I plan to do some actual comparison tests to experiment and prove or disprove my thinking...this will take some time as we need to develop some software or find some that has the "pre-control" tool included. If anyone knows of a software package, let me know.
What kind of process are you trying to evaluate? :cool:
 

bobdoering

Stop X-bar/R Madness!!
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#35
Wow! Someone willing to do a test! Cool! :applause:

Didn't someone once say a test is worth a bunch of expert opinions? Nah...probably just some nut...:notme:
Actually, I think what is more applicable is "Perfect practice makes perfect." You can do a test, but if it is designed incorrectly if will provide elegant statistical results that are wrong. So, a test in and of itself is admirable, but its worth is not guaranteed. :cool:
 
T

Tom Slack

#36
I think we should make room in our toolboxes for both Pre-Control and Control charts.

Pre-Control works when there is little process knowledge but the process has to run. The only information available is product specifications, but process information is usually collected during the pre-control phase. The "pre" in pre-control implies that "control" is going to come later.

Control charts work well when the results of a process study is available. It is not necessary to know product specifications width. BTW, a post stated that the measurements must be normally distributed for control charts to work. In fact by subgrouping, the Central Limit Theorem kicks in making them normal, besides a little deviation from normal will hurt us much.

Hope this helps,

Tom kjl.j'jll
 

bobdoering

Stop X-bar/R Madness!!
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#37
BTW, a post stated that the measurements must be normally distributed for control charts to work. In fact by subgrouping, the Central Limit Theorem kicks in making them normal, besides a little deviation from normal will hurt us much.
Actually, this does not work for precision machining. Controlling the process as if it is normal leads to over control, not control. Precision machining exhibits the uniform distribution, which holds no use for the mean. A subgroup of 5 points from a uniform distribution (if taken correctly) is a uniform distribution. Lots of people collect data and believe they have a normal distribution, but it is measurement error - which is normal - not the underlying process. It simply has the appearance of a normal distribution. Let me explain further:

How many diameters are in a circle?

An infinite number.

How many diameters does one typically report on a control chart?

One.

What kind of lottery winner would you have to be to think that one diameter out of an infinite number accurately describes the circular feature?

It is statistically an insignificant sample of an infinite population. And, frosting on the cake, the average of an insignificant sample is even less significant. But, that is the X-bar R chart! Garbage for precision machining.

That is why the X hi/lo-R chart resolves those statistical problems. It properly utilizes the uniform distribution in its usage. And, that is why I asked what kind of process is being evaluated. :cool:
 
G

Geoff Withnell

#38
I have always thought of SPC Vs Pre-Control as Control (focus on process) Vs Inspection (focus on specs) mentality. How does one demonstrate improvement using pre-control?
Actually, most of the time one doesn't. Since to use pre-control, the process should be very capable and stable, improvement efforts are usually concentrated elsewhere, where the process is not yet very capable or stable.

Geoff Withnell
 

bobdoering

Stop X-bar/R Madness!!
Trusted Information Resource
#39
In fact by subgrouping, the Central Limit Theorem kicks in making them normal, besides a little deviation from normal will hurt us much.
I hate to let these little rumors spread. For many distributions, it seems to be handy. For the uniform distribution, the theorem fails miserably. You do not need triple integrals and partial derivatives to show this to be the case. I have attached a data set of a uniform distribution. When correctly sampled (and that is important, we are evaluating the process distribution, not the sampling error distribution) at 5 pcs, and they are averaged, the resulting distribution is also uniform. Any correctly sampled subset of a uniform distribution is also a uniform distribution.

Only one way to cut that cake. Wasn't that fun? :cool:
 

Attachments

T

Tom Slack

#40
I hate to let these little rumors spread..... Any correctly sampled subset of a uniform distribution is also a uniform distribution.
After inspecting the dataset, it appears to be numbers that are trending upwards, then starts over. SPC is not a valid tool in this case because there is no variability hence no need for statistics. If we know one number, we will also know the next.

To demonstrate that the Central Distribution does work for a uniform distribution, try this experiment. Toss the die and record the results. After doing this several times, a frequency chart will show a roughly uniform distribution between 1 and 6.

Next pair the results up and find the averages of the paired observations. Again, create a frequency chart. This chart will have boundaries between 1 and 6, but will have a shape that looks like a normal distribution.

Interesting example,

Tom
 
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