Performance Analysis in Non-normal Linear Profiles Using Gamma Distribution

While the quality control procedures for monitoring profiles have been studied considerably, process capability analysis for non-normal profiles has not been explored at all. Profile monitoring is a relatively new set of techniques in quality control that is used in situations where the state of product or process is presented by a function of two or more quality characteristics. Such profiles can be modeled using linear or nonlinear regression models. In some applications, it is assumed that a single or multivariate quality characteristic(s) follows normal distribution. However, in certain applications this assumption may fail to hold and may yield misleading results. In this paper, we focus on the process capability analysis of profiles with effect of non-normality. Process capability indices give a quick indication of the capability of a manufacturing process. We use Burr distribution for process capability index (PCI) estimations when the process data exhibits non-normal distribution. Monte Carlo simulation for Gamma distribution is used to assess the efficacy of the proposed method.