Recent studies have shown that the variable sampling interval (VSI) scheme helps practitioners detect process shifts more quickly than the classical scheme (FRS). In this paper, the economically and statistically optimal design of the VSI T2 control chart for monitoring the process mean vector is investigated. The cost model proposed by Lorenzen and Vance (1986) is minimized through a genetic a...

TThe uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. This paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. As a novelty, the proposed controller employs a simple Gaussian Radial-Basis-Function Network as an uncertainty estimator. The proposed netw...

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This paper describes the use of unsupervised adaptive resonance theory ART2 neural networks for recognizing patterns in statistical process control charts. To improve the classi® cation accuracy, three schemes are proposed. The ® rst scheme involves using information on changes between consecutive points in a pattern. The second scheme modi® es the ART2 vigilance parameter during training. The ...

This article examines the performance of two one-class classification-based control charts through a real industrial application. These two control charts are the kernel distance–based control chart, known as the K chart, and the k-nearest neighbour data description-based control chart, referred to as the KNN chart. We studied the effectiveness of both charts in detecting out-ofcontrol observat...

Acceptance control charts (ACC), as an effective tool for monitoring highly capable processes, establish control limits based on specification limits when the fluctuation of the process mean is permitted or inevitable. For designing these charts by minimizing economic costs subject to statistical constraints, an economic-statistical model is developed in this paper. However, the parameters of s...

In this research, an iterative approach is employed to recognize and classify control chart patterns. To do this, by taking new observations on the quality characteristic under consideration, the Maximum Likelihood Estimator of pattern parameters is first obtained and then the probability of each pattern is determined. Then using Bayes’ rule, probabilities are updated recursively. Finally, when...

The principal function of a control chart is to help management distinguish different sources of variation in a process. Control charts are widely used as a graphical tool to monitor a process in order to improve the quality of the product. Chen and Hsieh (2007) have designed a T2 control chart using a Variable Sampling Size and Control limits (V SSC) scheme. They have shown that using the V SSC...

A nonparametric control chart based on a bivariate signed-rank test is developed for monitoring the changes in the location of a bivariate process. The average run length performance of the proposed nonparametric chart is investigated using a simulation study and is compared with a parametric control chart under bivariate normal and bivariate double exponential distributions. Further the perfor...

C-control chart assumes that process nonconformities follow a Poisson distribution. In actuality, however, this Poisson distribution does not always occur. A process control for semiconductor based on a Poisson distribution always underestimates the true average amount of nonconformities and the process variance. Quality is described more accurately if a compound Poisson process is used for pro...