Target Specification based on Cpk Requirements

Golfman25

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#1
I need some help. We have several metal stamped parts which require a cpk of 1.33 or 1.67 on critical characteristics. The print will give us p/m .002 (.004 total) or p/m .0015 (.003 total). However, due to the cpk requirements we can't use all of that tolerance. When we setup to run the part, we need to bring the characteristic into spec. and run from there. What target tolerance band can I give them to shoot for (needs to be less than that on the print)?

For example,
.750 p/m .0015 must maintian Cpk of 1.67. OR
.800 p/m .002 must maintain CPK of 1.33.

In general, what would be the max p/m they could setup to at least give us a chance to hit the cpk requirement?

Thanks in advance.
 
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Statistical Steven

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#2
I need some help. We have several metal stamped parts which require a cpk of 1.33 or 1.67 on critical characteristics. The print will give us p/m .002 (.004 total) or p/m .0015 (.003 total). However, due to the cpk requirements we can't use all of that tolerance. When we setup to run the part, we need to bring the characteristic into spec. and run from there. What target tolerance band can I give them to shoot for (needs to be less than that on the print)?

For example,
.750 p/m .0015 must maintian Cpk of 1.67. OR
.800 p/m .002 must maintain CPK of 1.33.

In general, what would be the max p/m they could setup to at least give us a chance to hit the cpk requirement?

Thanks in advance.
What is the variability (std deviation)?
Do you want a 95% confidence of meeting the Cpk?
Are you sure that the process is capable of meeting the specification?

Not so easy....
 

bobdoering

Stop X-bar/R Madness!!
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#3
Cpk is an oversimplified concept only loosely linked to reality that your customers like to use as sleep aids. Fact is, your capability depends on the sources of variation and the nature of their distributions (yes, plural - as found in the total variance equation). Typically, the customer assumes every process has one, normal distribution - with nothing to base that assumption on. Then - depending on the distributions, it depends on where its central tendency is (mean), skew and the variation about it (std dev).

A lot of this can be answered by what "adjustment" you make to get the part "in spec".

When it is all said and done, the answer is "it depends".
 

bobdoering

Stop X-bar/R Madness!!
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#4
To the basics: capability is tolerance divided by process spread. For a Cpk of 1.67 - if your process was perfectly centered the process spread would be 60% of tolerance, 1.33 is 75% of the tolerance. As you move away from a perfectly centered process, you have to have less process spread to maintain the capability. The 6 sigma you see in the capability equations is the process spread.

That is the simple stuff. The hard stuff is identifying the distribution. If you have poor measurement variation, everything will look "normal" - which is often why people think they have a normal distribution for their process but really do not. You need to make all variation statistically insignificant except for your actual process variation. Then you can make some good decisions on where to set up the process. Some processes need to be set up small to wear large - or vice versa. But, in order to do this you need to justify it by understanding the origins of variation in your process..
 
Last edited:
S

Sturmkind

#6
Bob is right on. Pity that Cpk is used as a tool of statistical terrorism by so many people when it was meant as one of many tools for process improvment.

I have attached a table that you may find useful though it is in millimeters.
 

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