Micro-manufacturing processes are characterized by high process variability and an increased significance of measurement uncertainty in relation to tight tolerance specifications. Therefore, an approach that separates the superposition of measurement and manufacturing variation is demanded. A novel design for a quality control chart that makes it possible to monitor, control and extract measure...

Multivariate statistical process control deserves particular attention in the recent scenario. Though, Hotelling control chart is quite popular and widely used technique in this field but its performance is deteriorated when the underlying distribution of the quality characteristics is not following multivariate normal distribution. Hence the need of developing a non-parametric multivariate con...

Most of the inventory control models assume that quality defect never happens, which means production process is perfect. However, in real manufacturing processes, the production process starts its operation in the in-control state; but after a period of time, shifts to the out-of-control state because of occurrence of some disturbances. In this paper, in order to approach the model to real man...

This paper deals with the application of Principal Component Analysis (PCA) and the Hotelling’s T2 Chart, using data collected from a drinking water treatment process. PCA is applied primarily for the dimensional reduction of the collected data. The Hotelling’s T2 control chart was used for the fault detection of the process. The data was taken from a United Utilities Multistage Water Treatment...

The common consideration on economic model is that there is knowledge about the risk of occurrence of an assignable cause and the various cost parameters that does not always adequately describe what happens in practice. Hence, there is a need for more realistic assumptions to be incorporated. In order to reduce cost penalties for not knowing the true values of some parameters, this paper aims ...

This paper presents a semi-supervised learning algorithm for a control chart pattern recognition system. A learning neural network is trained with labeled control chart patterns based on unsupervised learning. We then use the classification method based on a statistical correlation coefficient approach to test patterns. We find that the proposed semi-supervised learning algorithm is effective a...

In some real applications of Statistical Process Control it is necessary to design a control chart to not detect small process shifts, but keeping a good performance to detect moderate and large shifts in the quality. In this work we develop a new quality control chart, the synthetic T control chart, which can be designed to cope with this objective. A multi-objective optimization is carried ou...

In this research, we propose a bi-objective model for the economic-statistical design of the X-bar and S control charts. The model minimizes out-of-control average time to signal as well as minimizing mean hourly loss-cost where it incorporates the Taguchi loss function. Statistical constraint is considered in the model to achieve desired in-control time to signal. A non-dominated sort...

In this paper a multivariate quality control procedure is offered in which several correlated stages are present in production systems and in each stage there are several correlated control variable. Using Hotelling's T2 statistic, first, each stage is tested for being out of control. Then out of control variables are selected using Murphy's method. The remainder of the research involves evalua...

Multivariate control charts such as Hotelling`s T^ 2 and X^ 2 are commonly used for monitoring several related quality characteristics. These control charts use correlation structure that exists between quality characteristics in an attempt to improve monitoring. The purpose of this article is to discuss some issues related to the G chart proposed by Levinson et al. [9] for detecting shifts in ...