EWMA Control Charts for Monitoring High Yield Processes

Analytical formulae for computing the average time to signal (ATS) value for the exponentially weighted moving average (EWMA) cumulative count of conforming (CCC) chart using the Markov chain procedure are derived, as the performance of this chart is currently studied using simulation in the literature. Additionally, the variable sampling interval (VSI) EWMA CCC chart for monitoring the cumulative counts of items inspected until a nonconforming item is obtained in a high yield process and is developed to increase the sensitivity of the basic EWMA CCC chart. By using the Markov chain procedure, optimal parameters of the VSI EWMA CCC chart in minimizing the ATS value are obtained and provided to facilitate the chart's implementation in practice. It is found that the VSI EWMA CCC chart shows an impressive improvement over the basic CCC, VSI CCC, and EWMA CCC charts. An example using real data from an injection molding process producing micro‐prism array of an optical element is given to demonstrate the implementation of the VSI EWMA CCC chart in practice. The originality of this manuscript lies in the derivation of new Markov chain‐based analytical formulae for computing the ATS of the EWMA CCC chart and the incorporation of the VSI technique into the EWMA CCC chart to boost the out‐of‐control detection speed of the latter.

Section: Ewma CCC Chartmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new exponentially weighted moving average chart with an adaptive control scheme for high yield processes—An application in injection molding process

Abubakar

Khoo

Saha

et al. 2020

“…Chen introduced an EWMA chart based on the Poisson geometric distribution for the compound Poisson process. Mavroudis and Nicolas discussed the efficiency and verified the performance behaviour of the one‐sided EWMA control chart for high‐quality processes. Their designed chart is an efficient tool for detection of both upward and downward shifts for the non‐transformed geometric process.…”

Section: Exponentially Weighted Moving Average and Cumulative Summentioning

confidence: 99%

“…Their designed chart is an efficient tool for detection of both upward and downward shifts for the non‐transformed geometric process. Later, Mavroudis and Nikolaos extended their work on the two‐sided EWMA to one‐sided EWMA charts.…”

Section: Exponentially Weighted Moving Average and Cumulative Summentioning

confidence: 99%

An Overview of Control Charts for High‐quality Processes

Ali

Pievatolo

Göb

2016

115

Major difficulties in the study of high‐quality processes with traditional process monitoring techniques are a high false alarm rate and a negative lower control limit. The purpose of time‐between‐events control charts is to overcome existing problems in the high‐quality process monitoring setup. Time‐between‐events charts detect an out‐of‐control situation without great loss of sensitivity as compared with existing charts. High‐quality control charts gained much attention over the last decade because of the technological revolution. This article is dedicated to providing an overview of recent research and presenting it in a unifying framework. To summarize results and draw a precise conclusion from the statistical point of view, cross‐tabulations are also given in this article. Copyright © 2016 John Wiley & Sons, Ltd.

“…These two popular techniques have been extensively used in the control chart literature, for example, Borror et al showed that the EWMA control chart is the robust chart when n = 1, Molnau et al studied the multivariate EWMA chart for monitoring the mean vector, Yang presented the EWMA chart for monitoring the process mean and variance, and Singh and Prajapati concluded that CUSUM scheme is effective for quick response as compared with the EWMA scheme. The control charts based upon EWMA technique have been explored by many researchers including Abbas et al , Abbasi, Aslam et al , Kazemzadeh et al , Knoth and Steinmetz, Liu and Tien, Mavroudis and Nicolas, Shamsuzzaman and Wuand Su‐Fen et al…”

Section: Introductionmentioning

confidence: 99%

A Control Chart for COM–Poisson Distribution Using Multiple Dependent State Sampling

Aslam

Ahmad

Arif

2016

aIn this article, an attribute control chart has been proposed for the COM-Poisson distributed non-conformities using multiple dependent states sampling based on the exponentially weighted moving average statistic. Average run lengths of the out-ofcontrol process with different shift levels of the COM-Poisson distribution have been calculated through simulation. From comparison, it has been observed that the proposed control chart is better in detecting the out-of-control process quickly as compared with the existing control chart. A simulated example has been given for the practical use of the proposed control chart.