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In most modern manufacturing systems, products are often the output of some multistage processes. In these processes, the stages are dependent on each other, where the output quality of each stage depends also on the output quality of the previous stages. This property is called the cascade property. Although there are many studies in multistage process monitoring, there are fewer works on profile monitoring in multistage processes, especially on the variability monitoring of a multistage profile in Phase-I for which no research is found in the literature. In this paper, a new methodology is proposed to monitor the standard deviation involved in a simple linear profile designed in Phase I to... 

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... 

Control charts are statistical tools to monitor a process or a product. However, some processes cannot be controlled by monitoring a characteristic; instead, they need to be monitored using profiles. Economic-statistical design of profile monitoring means determining the parameters of a profile monitoring scheme such that total costs are minimized while statistical measures maintain proper values. While varying sampling interval usually increases the effectiveness of profile monitoring, economic-statistical design of variable sampling interval (VSI) profile monitoring is investigated in this paper. An extended Lorenzen–Vance function is used for modeling total costs in VSI model where the... 

Purpose: The purpose of this paper is to apply Poisson generalized linear model (PGLM) with log link instead of multinomial logistic regression to monitor multinomial logistic profiles in Phase I. Hence, estimating the coefficients becomes easier and more accurate. Design/methodology/approach: Simulation technique is used to assess the performance of the proposed algorithm using four different control charts for monitoring. Findings: The proposed algorithm is faster and more accurate than the previous algorithms. Simulation results also indicate that the likelihood ratio test method is able to detect out-of-control parameters more efficiently. Originality/value: The PGLM with log link has... 

Recently, many researchers and practitioners have shown interest on profile monitoring as a relatively new subarea of statistical process control. One main reason for this interest, and perhaps a key factor for the contributions of many researchers to this field, is the various applications of profile monitoring in real life. Although one can easily encounter many univariate applications of profile monitoring in service and manufacturing environments, there exist situations where quality of a product or process needs to be modeled in multivariate terms. In this paper, we investigate monitoring of multivariate simple linear profiles in phase II when independence assumption of observations... 

When a multistage manufacturing process is monitored statistically, the cascade property results in a more complicated condition compared to the case when a single-stage process is controlled. The cascade property usually exists in different stages of a multistage process, where the quality of a stage influences the performance of the next stage. Moreover, sometimes the quality of a product/process is best characterized by a functional relationship. This relationship is referred to as a profile. In this paper, phase I monitoring of simple linear profile is addressed for a multistage process involving the cascade property. To aim this, the capabilities of the methods that may be used to... 

In some quality control applications, quality of a product or a process can be characterized by a profile defined as a functional relationship between a response variable and one or more explanatory variables. Many researchers have contributed to the development of linear and nonlinear profiles to monitor a process or product. However, less work has been devoted to the development of process capability indices in profile monitoring to evaluate process performance with respect to specification limits. This paper presents a process capability analysis when the quality characteristic of interest is represented by a linear profile. Simulation analyses along with a real case study in leather... 

When a multistage manufacturing process is monitored statistically, the cascade property results in a more complicated condition compared to the case when a single-stage process is controlled. The cascade property usually exists in different stages of a multistage process, where the quality of a stage influences the performance of the next stage. Moreover, sometimes the quality of a product/process is best characterized by a functional relationship. This relationship is referred to as a profile. In this paper, phase I monitoring of simple linear profile is addressed for a multistage process involving the cascade property. To aim this, the capabilities of the methods that may be used to... 

Statistical process control methods for monitoring processes with multivariate measurements in both the product quality variable space and process variable space are considered in this paper. Some processes, however, are better characterized by a profile or a function of quality variables. For each profile, we assume that a collection of data on the response variable along with the values of the corresponding quality variables is measured. While the linear function is the simplest, it occurs frequently that many of the nonlinear functions may be transferred to linear functions easily. This paper proposes a control chart based on the generalized linear test (GLT) to monitor coefficients of... 

In many situations, the quality of a process can be characterized better by a relationship, known as a profile, between a response variable and one or more predictors. Almost all research efforts assume that response variable is continuous and follows a normal distribution, while there are instances in which the response is a binary variable, and methods such as logistic regression are commonly used. In this paper, four control schemes namely Hotelling T2, MEWMA, Likelihood Ratio Test (LRT) and LRT/EWMA are proposed to monitor binary response profiles in phase II. The performance of the proposed control charts is evaluated and compared by simulation experiments for different shift values in... 

Statistical process control methods for monitoring processes with univariate or multivariate measurements are used widely when the quality variables fit to known probability distributions. Some processes, however, are better characterized by a profile or a function of quality variables. For each profile, it is assumed that a collection of data on the response variable along with the values of the corresponding quality variables is measured. While the linear function is the simplest, it occurs frequently that many of the nonlinear functions may be transferred to linear functions easily. This paper proposes a control chart based on the generalized linear test (GLT) to monitor coefficients of... 

In economic design of profiles, parameters of a profile are determined such that the total implementation cost is minimized. These parameters consist of the number of set points, n, the interval between two successive sampling, h, and the parameters of a control chart used for monitoring. In this paper, the Lorenzen-Vance cost function is extended to model the costs associated with implementing profiles. The in-control and the out-of-control average run lengths, ARL0 and ARL1, respectively, are used as two statistical measures to evaluate the statistical performances of the proposed model. A genetic algorithm (GA) is developed for solving both the economic and the economic-statistical... 

Statistical analysis of profile monitoring, a relatively new sub-area of statistical process control due to its applications in different industries, have urged researchers and practitioners to contribute to the developments of new monitoring methods. A statistical profile is a relationship between a quality characteristic (a response) and one or more independent variables to characterize quality of a process or a product. In this article, statistical profiles based on nominal responses are studied, where logistic regression is used to model the responses. Three approaches including likelihood ratio test (LRT), multivariate exponentially weighted moving average (MEWMA), and support vector... 

The Phase I applications of the statistical profile monitoring have recently been extended to the case when the response variable is binary. We are motivated to undertake the current research in an attempt to try to provide a unified framework for the Phase I control in the context of statistical profile monitoring that can be used to tackle a large class of response variables, such as continuous, count, or categorical response variables. The unified framework is essentially based on applying the change point model to the class of generalized linear models. The proposed Phase I control chart is assessed and compared with the existing charts under binomial and Poisson profiles. Some... 

In this article, the general linear profile-monitoring problem in multistage processes is addressed. An approach based on the U statistic is first proposed to remove the effect of the cascade property in multistage processes. Then, the T2 chart and a likelihood ratio test (LRT)-based scheme on the adjusted parameters are constructed for Phase-I monitoring of the parameters of general linear profiles in each stage. Using simulation experiments, the performance of the proposed methods is evaluated and compared in terms of the signal probability for both weak and strong autocorrelations, for processes with two and three stages, as well as for two sample sizes. According to the results, the...