Estimation of Process Parameters to Determine the Optimum Diagnosis Interval for Control of Defective Items

Abstract

The online quality monitoring procedure for attributes proposed by Taguchi has been critically studied and extended by a few researchers. Determination of the optimum diagnosis interval requires estimation of some parameters related to the process failure mechanism. Improper estimates of these parameters may lead to an incorrect choice of the diagnosis interval and thus huge economic penalties. We propose a Bayesian approach to estimate the process parameters under two different process models, commonly called as the case II and case III models in the literature. We discuss a systematic way to use available engineering knowledge in eliciting the prior for the parameters, and demonstrate the performance of the proposed method using extensive simulation and a case study from a hot rolling mill.