On the Performance of Geometric Charts with Estimated Control Limits

Yang, Zhenlin; Xie, Min; Kuralmani, V.; Tsui, Kwok‐Leung

doi:10.1080/00224065.2002.11980176

Cited by 86 publications

(101 citation statements)

References 16 publications

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“…In reality, it is an estimated quantity based on the test mentioned in Section 2. It is well known that control limits based on estimates may have a drastic impact on the performance of a control chart (see Yang et al 16 for a discussion in the context of CCC r charts). Since that test had a sample size of 14 215, we feel that in our case it justified to treat p in as a constant.…”

Section: Methodsmentioning

confidence: 99%

Monitoring Infrequent Failures of High‐volume Production Processes

Bucchianico

Mooiweer

Moonen

2005

Quality & Reliability Eng

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We present a case study in monitoring a high-volume production process with a high yield. Testing the products is difficult and is only possible in a destructive way with a defect/no-defect result. We review several attribute charting procedures for high-yield processes. It turns out that monitoring the number of conforming items between the occurrence of non-conforming items is appropriate. We discuss several implementation aspects of CCC r charts in our case study. The implementation is based on a new formula for the standard deviation of the number of inspected items before a signal.

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Section: Methodsmentioning

confidence: 99%

Monitoring Infrequent Failures of High‐volume Production Processes

Bucchianico

Mooiweer

Moonen

2005

Quality & Reliability Eng

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“…The conforming run length (CRL) chart proposed by Bourke (1991) was originally developed for attribute quality control to detect the shift in fraction non-conforming p, and was studied recently by many researchers (Kaminsky, Benneyan, & Davis, 1992;Wu, Yeo, & Fan, 2000;Xie, Goh, & Lu, 1998;Yang, Xie, Kuralmani, & Tsui, 2002). The CRL is defined as the number of conforming units between two non-conforming units, inclusive of the ending non-conforming unit.…”

Section: Crl Chartmentioning

confidence: 99%

A synthetic control chart for monitoring process dispersion with sample standard deviation

Huang

Chen

2005

Computers & Industrial Engineering

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“…The conforming run-length (CRL) chart proposed by Bourke [17] was originally developed for attribute quality control to detect the shift in fraction nonconforming p, and was studied recently by many researchers [18][19][20][21]. The CRL is defined as the number of conforming units between two nonconforming units, inclusive of the ending nonconforming unit.…”

Section: Crl Chartmentioning

confidence: 99%

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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On the Performance of Geometric Charts with Estimated Control Limits

Cited by 86 publications

References 16 publications

Monitoring Infrequent Failures of High‐volume Production Processes

Monitoring Infrequent Failures of High‐volume Production Processes

A synthetic control chart for monitoring process dispersion with sample standard deviation

A synthetic control chart for monitoring process dispersion with sample range

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