Using a novel approach to assess process performance in the presence of measurement errors
In recent years, process capability indices have been widely used to provide numerical measures on process performance. A substantial majority of capability research works that appeared in the literature do not take into account gauge measurement errors. However, such assumptions do not adequately reflect real situations since measurement errors unfortunately cannot be avoided in most manufacturing processes. Estimating and testing process capability without considering gauge measurement errors may often lead to unreliable decisions. Therefore, this paper applies the concept of generalized confidence intervals (GCI) to measure process capability based on the most widely used index Cpk in the presence of measurement errors. An exhaustive simulation was conducted to assess the performance of the GCI method in terms of the coverage probability (CP) and the expected value of the generalized lower confidence limit. The results indicate that GCI method can provide more accurate lower confidence limits, and CPs are very close to the nominal confidence level, although slightly conservative in a few cases. The overall conclusion is that the GCI method appears quite satisfactory for measuring process capability when measurement errors are present or unavailable.
gauge measurement errors generalized confidence intervals coverage probability process yield