Gamma generalized linear model‐based control charts for high‐purity processes

Abstract

AbstractStatistical process monitoring of high‐purity manufacturing processes becomes challenging if the defect rate depends on the fluctuations of a set of covariates (e.g., inspected weight, volume, temperature). This paper applies the generalized linear model framework to statistical process control for detecting contextual anomalies in high‐purity processes. Different types of predictive residuals (i.e., Pearson, deviance, and quantile) and recursive residuals are considered, and the performance of these schemes is compared via a simulation study.