Sharif Digital Repository

In most modern manufacturing systems, products are often the output of several correlated stages. Nevertheless, quality of a product or process in both single and multistage processes is usually expressed by a single quality characteristic, two or more characteristics, or profiles. Although there are many studies in univariate and multivariate-multistage process monitoring, fewer works focus on profile monitoring of multistage processes. This paper addresses the problem of monitoring a simple linear profile that is going through a multistage process in phase II. Using a first-order autoregressive correlation model, the relationship between the stages is first modeled. Then, the cascade... 

In this paper, a new control scheme is proposed for Phase-II monitoring of simple linear profiles in multistage processes. In this scheme, an approach based on the U transformation is first applied to remove the effect of the cascade property involved in multistage processes. Then, a single max-EWMA-3 control statistic is derived based on the adjusted parameter estimates for simultaneous monitoring of all the parameters of a simple linear profile in each stage. Not only is the proposed scheme able to detect both increasing and decreasing shifts but it also has the feature of identifying the out-of-control parameter responsible for the source of process shift. Using extensive simulation... 

In this paper, an approach based on the U statistic is first proposed to eliminate the effect of between-profile autocorrelation of error terms in Phase-II monitoring of general linear profiles. Then, a control chart based on the adjusted parameter estimates is designed to monitor the parameters of the model. The performance of the proposed method is compared with the ones of some existing methods in terms of average run length for weak, moderate, and strong autocorrelation coefficients under different shift scenarios. The results show that the proposed method provides significantly better results than the competing methods to detect shifts in the regression parameters, while the competing... 

Functional data and profiles are characterized by complex relationships between a response and several predictor variables. Fortunately, statistical process control methods provide a solid ground for monitoring the stability of these relationships over time. This study focuses on the monitoring of 2-dimensional geometric specifications. Although the existing approaches deploy regression models with spatial autoregressive error terms combined with control charts to monitor the parameters, they are designed based on some idealistic assumptions that can be easily violated in practice. In this paper, the independent component analysis (ICA) is used in combination with a statistical process... 

In this paper, two control charts based on the generalized linear test (GLT) and contingency table are proposed for Phase-II monitoring of multivariate categorical processes. The performances of the proposed methods are compared with the exponentially weighted moving average-generalized likelihood ratio test (EWMA-GLRT) control chart proposed in the literature. The results show the better performance of the proposed control charts under moderate and large shifts. Moreover, a new scheme is proposed to identify the parameter responsible for an out-of-control signal. The performance of the proposed diagnosing procedure is evaluated through some simulation experiments. © 2017 Taylor & Francis... 

Various control charts have been proposed to monitor generalized linear pro les in Phase II. However, the robustness of the proposed methods in detecting di erent types and especially di erent directions of changes is not well-studied in the literature. In real-world applications, di erent kinds of change such as drift and multiple changes are likely to occur, which can be isotonic (increasing) or antitonic (decreasing). This paper studies the robustness of the Rao Score Test (RST) method, T2, and Multivariate Exponential Weighted Moving Average (MEWMA) in di erent types, drift and multiple, and directions of changes. The RST method also bene ts from a change-point detection approach whose...