Multivariate attribute control chart using Mahalanobis<i>D</i><sup>2</sup>statistic

Mukhopadhyay, Arup Ranjan

doi:10.1080/02664760701834980

Cited by 26 publications

(31 citation statements)

References 1 publication

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“…The paint defect data in Table 3 is taken from Mukhopadhyay (2008) but the sample size is changed to 100. This example deals with the fraction defective of two types of paint defects of a ceiling fan cover.…”

Section: Numerical Examplementioning

confidence: 99%

“…Niaki and Abbasi (2007) first proposed a new transformation technique to reduce the amount of skewness of distribution of the attributes data and then use a Hotelling T 2 control chart on the transformed data. Mukhopadhyay (2008) expanded the concept of 'Mahalanobis Distance' in a multinomial distribution and thereby proposed a multivariate-attribute control chart. A drawback of this work is that when there was an out-of-control signal, it is often difficult to determine which component of the process was out of control.…”

Section: Introductionmentioning

confidence: 99%

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Using cause selecting control charts to monitor dependent process stages with attributes data

Yang

Yeh

2011

Expert Systems with Applications

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Section: Numerical Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using cause selecting control charts to monitor dependent process stages with attributes data

Yang

Yeh

2011

Expert Systems with Applications

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“…For example, Aebtarm and Bouguila (2011) present a review of methods employed to improve the sensitivity of attribute C control chart and inspection cost. A useful approach is the one proposed by Mukhopadhyay (2008). This work details that the multi-attribute 2 D control chart does not use the chi-squared distribution; this implies that a minimum expected frequency is not necessary to ensure its performance.…”

Section: Introductionmentioning

confidence: 99%

A Multi-attribute Control Chart for Monitoring Friction Stir Welding Process Considering Small Sample Sizes

Ramírez-Méndez¹,

Cantú-Sifuentes

González³

et al. 2018

Soldag. insp.

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Often, welding processes used in the industry affect the mechanical properties of materials and quality of a manufactured product. There is, however, an alternative process named Friction Stir Welding (FSW), which is an solid state welding process developed to weld light alloys without compromising their mechanical properties. It is of interest to monitor the performance of FSW process to detect loss of quality. In practice, superficial and internal defects can be found; they can be identified through simple visual inspection and through visual recognition on destructive testing respectively, both procedures represent inspection by attributes. Therefore a multi-attribute control chart is assessed to monitor the process. Commonly, multi-attribute control charts involve high sampling rates to ensure accurate monitoring. In this paper, a multi-attribute control chart is proposed, considering the use of empirical control limits, instead of the theoretical ones, in order to improve its accuracy and lessen the small sample sizes effect. The performance of proposed approaches is analyzed by means of Monte Carlo simulation. The results suggest that the performance of the empirical designs is better than the theoretical ones in all tested cases. Finally, the results of monitoring FSW process data are detailed.

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“…Copyright Speaking of multinomial distribution, another case of grouped data is the one resulting from the classification of products into several categories of non-conformities when monitoring the proportion non-conforming of all the categories. A recent related study is the one by Mukhopadhyay 33 , for which we are now going to talk about.…”

Section: Introductionmentioning

confidence: 99%

“…Mahalanobis (1946) made some studies on test of hypothesis, for the case of more than one attribute variables involved, based on the Hotelling T 2 test. Mukhopadhyay 33 expanded the concept of 'Mahalanobis distance' in case of a multinomial distribution and proposed a multivariate attribute control chart, since, when products are classified into several categories of non-conformities, a control scheme is required to exercise simultaneous control of all the categories. A traditional approach to this problem has been to apply several p-charts-one for each category of defect.…”

Section: Introductionmentioning

confidence: 99%

Review of multinomial and multiattribute quality control charts

Topalidou

Psarakis

2009

Quality & Reliability Eng

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Attribute control charts are very useful nowadays for monitoring processes where the quality characteristics cannot be measured in a continuous scale, which may be manufacturing processes from industrial settings, health-care processes or processes from service industries and environments of non-manufacturing quality-improvement efforts. Many of the above cases, however, involve the monitoring of multiple attributes simultaneously, thus leading to the case of multinomial and multiattribute quality control methods, which are better than the simultaneous use of multiple uni-attribute methods. In this study, an attempt to review the research previously conducted on multiattribute quality control is made in order to help the interested researchers and practitioners get informed about the references on the relevant research in this field, regarding the design, performance and applications of multiattribute control charts.

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Multivariate attribute control chart using MahalanobisD²statistic

Cited by 26 publications

References 1 publication

Using cause selecting control charts to monitor dependent process stages with attributes data

Using cause selecting control charts to monitor dependent process stages with attributes data

A Multi-attribute Control Chart for Monitoring Friction Stir Welding Process Considering Small Sample Sizes

Review of multinomial and multiattribute quality control charts

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Multivariate attribute control chart using MahalanobisD2statistic

Cited by 26 publications

References 1 publication

Using cause selecting control charts to monitor dependent process stages with attributes data

Using cause selecting control charts to monitor dependent process stages with attributes data

A Multi-attribute Control Chart for Monitoring Friction Stir Welding Process Considering Small Sample Sizes

Review of multinomial and multiattribute quality control charts

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About

Multivariate attribute control chart using MahalanobisD²statistic