Profiles are more comprehensive tools for modeling process and product quality. Profile monitoring is the way of controlling profiles, which is done by establishing control charts. In most of the cases, the in-control values of the profile parameters are asuumed to be known in phase II, wheras it is not valid in many practical situations. In other word, the process of estimating the parameters in phase I, is ignored in Phase II. In this project, we investigate the effect of parameters estimation from in-control Phase I samples on the in-control and out-of-control performance of three Phase II control charts for monitoring nonlinear mean profiles designated as T 2 , MCUSUM, and EWMA. The out-of-control performance of the methods is evaluated by using corrected limits to consider the variability due to parameters estimation. The performance of the monitoring approaches is compared in terms of statistical propertis of ARL distribution including AARL, SDARL, CVARL in order to consider practitioner-to-practitioner variability trough a simumation algorithm. The resurls showed that parameters estimation severly effect on the performance of monitoring schemes.
