Estimating the change point of the cumulative count of a conforming control chart under a drift

Amiri, Amirhossein; Khosravi, Ramezan

doi:10.1016/j.scient.2011.12.015

Cited by 15 publications

(3 citation statements)

References 26 publications

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“…Amiri and Khosravi proposed an MLE for the change point of the nonconforming fraction in the high‐quality process with a linear‐trend based on the geometric process. The proposed estimator is compared with the estimator based on a single step change of the process nonconforming fraction.…”

Section: Time‐between‐events Control Chartsmentioning

confidence: 99%

An Overview of Control Charts for High‐quality Processes

Ali

Pievatolo

Göb

2016

Quality & Reliability Eng

115

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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.

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Section: Time‐between‐events Control Chartsmentioning

confidence: 99%

An Overview of Control Charts for High‐quality Processes

Ali

Pievatolo

Göb

2016

Quality & Reliability Eng

115

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“…The CCC charts continue to gain acceptance in the monitoring of high‐quality processes. Other studies on control charts for high yield processes can be found in Xie et al, 10 Chang and Gan, 11 Noorossana et al, 12 Wang, 13 Amiri and Khosravi, 14 Acosta‐Mejia, 15 Amiri and Khosravi, 16 Bersimis et al, 17 Fallahnezhad and Golbafian, 18 and Zhang et al 19 For a comprehensive review on control charts for high yield processes, we refer the readers to Ali et al 20 and the references therein.…”

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

Quality & Reliability Eng

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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.

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“…Chen et al [5] presented a CCC chart with variable sampling intervals and control limits. Amiri and Khosravi [6] provided a maximum likelihood estimator for the change point of the nonconforming level in the highquality process with a linear trend. Ahmadi and Fallahnezhad [7,8] presented an acceptance-sampling plan based on cumulative count of conforming using minimum angle method.…”

Section: Introductionmentioning

confidence: 99%

Economic design of cumulative count of conforming control charts based on average number of inspected items

Nezhad

Golbafian

2017

Scientia Iranica

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KEYWORDSStatistical process control; High quality processes; Cumulative count of conforming control charts; Average number of inspected items; Economic design of control charts.Abstract. Nowadays, continuous improvement can be regarded as the essence of survival and growth in order to not only increase the competition in global market, but also requirement for ever-decreasing defect levels in processes. Therefore, new statistical analysis techniques and decision-making procedures have been continuously evolved, both to handle high quality processes and to look for process improvement opportunities. CCC-r chart, or extended approach of CCC charts, is generally a technique for high quality processes, when nonconforming items are rarely observed. This study develops a mathematical model based on the average number of inspected items for the economic design of CCC-r chart, so that the average cost per item is minimized. The optimal designed parameters for di erent nonconforming fractions and di erent parameters in each iteration are calculated. In addition, with respect to Type I error ( ) and Type II error ( ) in the process, sensitivity analysis of the model is carried out.

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Estimating the change point of the cumulative count of a conforming control chart under a drift

Cited by 15 publications

References 26 publications

An Overview of Control Charts for High‐quality Processes

An Overview of Control Charts for High‐quality Processes

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

Economic design of cumulative count of conforming control charts based on average number of inspected items

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