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

Summary: [This abstract is based on the authors' abstract.] Determination of the most economic sampling interval for control of defective items is crucial to manufacturing processes that produce a continuous stream of products at high speed. A Bayesian approach is proposed to estimate the process parameters under case II and case II process models. A systematic method to use available engineering knowledge in eliciting the prior for the parameters is discussed, and the performance of the proposed method is demonstrated using simulation and a case study from a hot rolling mill.