An improved run-to-run process control scheme for categorical observations with misclassification errors

When product quality characteristics are evaluated and assigned to exclusive categories, measurement errors (misclassification of products) always exist unless a perfect measurement system is used to identify the categories. In run-to-run (R2R) process control, a categorical controller has been developed for process adjustments with categorical variables. However, if process outputs are misclassified, an adjustment bias will be introduced by the controller. In this study, an improved categorical R2R controller that utilizes the misclassification probabilities to decrease process deviation is proposed. Simulation results show that the proposed controller exhibits better performance when misclassification exists. Copyright © 2008 John Wiley & Sons, Ltd.