Monitoring Profiles Based on Nonparametric Regression Methods

Abstract

The use statistical process control (SPC) in monitoring and diagnosis of process and product quality profiles remains an important problem in various manufacturing industries. The SPC problem with a nonlinear profile is particularly challenging. This article proposes a novel scheme to monitor changes in both the regression relationship and the variation of the profile online. It integrates the multivariate exponentially weighted moving average procedure with the generalized likelihood ratio test based on nonparametric regression. The proposed scheme not only provides an effective SPC solution to handle nonlinear profiles, which are common in industrial practice, but it also resolves the latent problem in popular parametric monitoring methods of being unable to detect certain types of changes due to a misspecified, out-of-control model. Our simulation results demonstrate the effectiveness and efficiency of the proposed monitoring scheme. In addition, a systematic diagnostic approach is provided to locate the change point of the process and identify the type of change in the profile. Finally, a deep reactive ion-etching example from semiconductor manufacturing is used to illustrate the implementation of the proposed monitoring and diagnostic approach.