The performance of control charts for monitoring process variation

Lowry, Cynthia A.; Champ, Charles W.; Woodall, William H.

doi:10.1080/03610919508813250

Cited by 64 publications

(43 citation statements)

References 13 publications

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“…ple is regarded as a conforming unit in the CRL chart, and the control flow returns to step (2). Otherwise, the sample is considered to be a nonconforming unit, and the control flow proceeds to the next step.…”

Section: Synthetic Control Chart For Monitoring Process Dispersionmentioning

confidence: 99%

“…This number is taken as a CRL sample of the CRL chart. (5) If L CRL, the process is thought to be under control, and the control flow goes back to step (2). Otherwise, the process is thought to be out of control, and the control flow proceeds to the next step.…”

Section: Synthetic Control Chart For Monitoring Process Dispersionmentioning

confidence: 99%

“…An increase in the process standard deviation, σ, should be detected because it leads the output of the process to be more scattered. A decrease in σ leading the output of the process to be more concentrated should also be detected because it may be a clue to improve the process [1,2]. Shewhart sample range (R) and sample standard deviation (S) charts are basic tools to monitor the changes in process dispersion.…”

Section: Introductionmentioning

confidence: 99%

“…Crowder and Hamilton [6] proposed a one-sided EWMA chart based on ln S 2 to detect the increase in σ. Chang and Gan [7] extended Crowder and Hamilton's [6] research to investigate the effect of reflecting boundaries and proposed a one-sided EWMA chart based on ln S 2 to detect the decrease in σ. Lowry et al [2] supplemented Shewhart R and S charts with runs rules for monitoring the increase and the decrease in σ. Chang and Gan [8] proposed a CUSUM chart based on ln S 2 to detect the increase and the decrease in σ, and discussed its fast initial response (FIR) features. Acosta-Mejia [9] suggested a CUSUM chart based on R to detect the decrease in σ and evaluated the performance of Nelson's [1] runs rule.…”

Section: Introductionmentioning

confidence: 99%

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A synthetic control chart for monitoring process dispersion with sample range

Chen

Huang

2005

Int J Adv Manuf Technol

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A synthetic control chart for monitoring the changes in the standard deviation of a normally distributed process is proposed in this paper. The synthetic chart consists of the sample range (R) chart and the conforming run-length (CRL) chart. The R chart can be viewed as a special case of the synthetic chart. The operation, design and performance of this chart are described. Average run-length comparisons between other procedures and the synthetic chart are presented. It indicates that the synthetic chart is a good alternative for monitoring process dispersion. The variable sampling interval (VSI) schemes, as an enhancement to the synthetic chart, are discussed to further improve the chart performance. An example is presented to illustrate the application of synthetic chart and its VSI scheme.Keywords Control chart · Conforming run length · Process dispersion · Variable sampling interval Nomenclature n sample size R sample range W relative range CRL conforming run length k + coefficient of upper control limit of the R chart for δ > 1 k − coefficient of lower control limit of the R chart for δ < 1 L lower control limit of the CRL chart δ magnitude of the change in the process standard deviation δ d magnitude of the change in the process standard deviation that should be detected as soon as possible F(·) cumulative distribution function of W ARL R average number of R samples required to detect for the R chart

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Section: Synthetic Control Chart For Monitoring Process Dispersionmentioning

confidence: 99%

Section: Synthetic Control Chart For Monitoring Process Dispersionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A synthetic control chart for monitoring process dispersion with sample range

Chen

Huang

2005

Int J Adv Manuf Technol

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“…The joint design of Shewhart mean and range scheme can be found in Saniga [4][5][6][7] . Exact run length results for the Shewhart charts with supplementary runs rules for the mean and variance were developed by Champ and Woodall 8 and Lowry et al 9 , respectively. The power functions of selected Shewhart x charts with runs rules were tabulated by Wheeler 10 .…”

Section: Introductionmentioning

confidence: 99%

Interval Charting Schemes for Joint Monitoring of Process Mean and Variance

Gan

Ting

Chang

2004

Quality & Reliability Eng

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Shewhart mean and variance schemes continue to enjoy great popularity among process engineers because they are easy to understand and are the most sensitive in detecting large process shifts. For some processes, for example the screen printing process in memory modules assembly, there is a possibility of the process mean and variance shifting at the same time due to special causes. Thus, instead of studying the joint behavior by looking at the mean and variance information on two separate charts, it is practical and usually more meaningful to look at the combined mean and variance information on an interval charting scheme. The interval scheme is found to have a similar run length performance to the Shewhart mean and variance schemes and only a single chart needs to be managed. A simple design procedure for the interval scheme that matches the corresponding Shewhart scheme is provided for easy implementation. Also, the interval and Shewhart schemes are compared based on real manufacturing data sets.

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Effective Control Charts for Monitoring Multivariate Process Dispersion

Yen

Shiau

Yeh

2011

Quality & Reliability Eng

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When monitoring process dispersion, it is common to pay more attention to dispersion increases than to decreases for practical reasons. Nonetheless, it is also important to detect dispersion decreases for two reasons: (i) it deserves further investigations as to why the process has improved; and (ii) if the process has changed, the settings of the control chart would need to be adjusted for effective future monitoring. In this paper, we first propose an effective control chart for detecting multivariate dispersion decreases in phase II process monitoring, which is constructed using the same approach as that of the one‐sided likelihood‐ratio‐test‐based multivariate chart proposed recently in the literature for detecting dispersion increases. We then discuss a combined charting scheme by combining these two one‐sided charts for detecting either dispersion increases or decreases. Comparative simulation studies show that the proposed combined control charting scheme outperforms several existing two‐sided control charts in terms of the average run length when the process dispersion indeed increases or decreases. Two real‐life examples are presented to demonstrate the applicability of the proposed charts. Copyright © 2011 John Wiley & Sons, Ltd.

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The performance of control charts for monitoring process variation

Cited by 64 publications

References 13 publications

A synthetic control chart for monitoring process dispersion with sample range

A synthetic control chart for monitoring process dispersion with sample range

Interval Charting Schemes for Joint Monitoring of Process Mean and Variance

Effective Control Charts for Monitoring Multivariate Process Dispersion

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