hexagon logo

calculating CPK's

I was wondering if anyone has had this problem.

I run a program get my 30 pcs data send it to data page and the calculation from data page is different when done with a calculator. I got a cp 5.18 and a cpk of 5.21. I thought the cp had to be bigger? Then when I did it on my calculator I got the cp at 5.55 and the cpk at 5.52Confused
Parents
  • No, actually, DataPage has a special case... When the nominal and lower tolerance are both zero and there is a positive upper tolerance. then Cpk is calculated using the upper tolerance. The lower tolerance isn't considered. Normally we calculate Cpk = Min(Cpu, Cpl) where Cpu is the calculation using the upper tolerance and Cpl is the calculation using the lower tolerance. In the special case of TP, form error, etc (which datapage assumes is the case based on NOM=LT=0) we use Cpk=Cpu. It's been like this for 18 years or so. This was implemented so you aren't punished for being too good.

    This can cause the goofy scenario of showing Cpk to be smaller than Cp.

    As you state, folks would be better off to consider the axes separately. The Cpk (and Cp) calculations have an underlying assumption that the data is normally distributed, i.e. a gaussian curve can represent the probability distribution function and this would mean that there it is probabilistically possible to have a negative Cpk. This of course can't happen. So the underlying assumptions aren't quite valid.
Reply
  • No, actually, DataPage has a special case... When the nominal and lower tolerance are both zero and there is a positive upper tolerance. then Cpk is calculated using the upper tolerance. The lower tolerance isn't considered. Normally we calculate Cpk = Min(Cpu, Cpl) where Cpu is the calculation using the upper tolerance and Cpl is the calculation using the lower tolerance. In the special case of TP, form error, etc (which datapage assumes is the case based on NOM=LT=0) we use Cpk=Cpu. It's been like this for 18 years or so. This was implemented so you aren't punished for being too good.

    This can cause the goofy scenario of showing Cpk to be smaller than Cp.

    As you state, folks would be better off to consider the axes separately. The Cpk (and Cp) calculations have an underlying assumption that the data is normally distributed, i.e. a gaussian curve can represent the probability distribution function and this would mean that there it is probabilistically possible to have a negative Cpk. This of course can't happen. So the underlying assumptions aren't quite valid.
Children
No Data