This is machine translation

Translated by Microsoft
Mouse over text to see original. Click the button below to return to the English verison of the page.

Capability Studies

Before going into production, many manufacturers run a capability study to determine if their process will run within specifications enough of the time. Capability indices produced by such a study are used to estimate expected percentages of defective parts.

Capability studies are conducted with the capability function. The following capability indices are produced:

  • mu — Sample mean

  • sigma — Sample standard deviation

  • P — Estimated probability of being within the lower (L) and upper (U) specification limits

  • Pl — Estimated probability of being below L

  • Pu — Estimated probability of being above U

  • Cp(U-L)/(6*sigma)

  • Cpl(mu-L)./(3.*sigma)

  • Cpu(U-mu)./(3.*sigma)

  • Cpkmin(Cpl,Cpu)

As an example, simulate a sample from a process with a mean of 3 and a standard deviation of 0.005:

rng default; % For reproducibility
data = normrnd(3,0.005,100,1);

Compute capability indices if the process has an upper specification limit of 3.01 and a lower specification limit of 2.99:

S = capability(data,[2.99 3.01])
S = 

  struct with fields:

       mu: 3.0006
    sigma: 0.0058
        P: 0.9129
       Pl: 0.0339
       Pu: 0.0532
       Cp: 0.5735
      Cpl: 0.6088
      Cpu: 0.5382
      Cpk: 0.5382

Visualize the specification and process widths:

capaplot(data,[2.99 3.01]);
grid on

Was this topic helpful?