Evaluating the performance of variable scheme X-bar control chart: a Taguchi loss approach

Amiri, Fahime; Noghondarian, Kazem; Safaei, Abdul Sattar

doi:10.1080/00207543.2014.906762

Cited by 11 publications

(8 citation statements)

References 40 publications

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“…To this end, the current study also attended to compare the sampling schemes based on the ANF. Reduction in the ANF, as highlighted by Fallahnezhad et al, 41 Amiri et al, 42 Faraz and Saniga, 43 Shojaee et al 44 and Katebi et al, 45 has improved the performance of the control chart. Being similar to other measures based on the Markov chain concepts, The formula for calculating this measure with the vector of false alarms f is as follows:

ANF = b {((), I - Q)}^{- 1} f

…”

Section: Statistical Measures For Performance Evaluation Of Sampling Schemesmentioning

confidence: 97%

Development of U control chart by variable sample size and sampling interval to improve the statistical properties

Shojaie

Imani

2021

Engineering Reports

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It has been proved that the quality control charts with variable sampling schemes are more effective than the classical ones in improving statistical measures. The Average Number of False Alarms (ANF), Average Number of Samples (ANS), Average Number of Inspected Items (ANI), and Adjusted Average Time to Signal (AATS) have been the most important statistical measures always being attending in the evaluation of control charts. Thus, this study was conducted aiming at illustrating a comprehensive analytical review on the U control chart via the statistical measures. To this end, different levels of the possible factors were determined, and the results of calculating the statistical measures and the obtained parameters on the sampling schemes of the U control chart were presented. The results indicated that the variable U control charts were capable of improving the effectiveness of statistical measures, especially for detecting shifts and number of false alarms.

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ANF = b {((), I - Q)}^{- 1} f

…”

Section: Statistical Measures For Performance Evaluation Of Sampling Schemesmentioning

confidence: 97%

Development of U control chart by variable sample size and sampling interval to improve the statistical properties

Shojaie

Imani

2021

Engineering Reports

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“…Selecting the best setting is often done through trial-and-error experiments, and is difficult due to the many possible combinations. Many analysts in field of ESD of control charts have already recommended using this method to determine the optimised value of GA parameters (Amiri, Noghondarian, and Safaei 2014;Chen and Chang 2008;Chou, Cheng, and Lai 2008). In order to find the best setting of these GA parameters, the L9 orthogonal 4450…”

Section: Tuning Of Ga Parametersmentioning

confidence: 99%

“…The sensitivity of the optimal cost, and the chart parameters for different loss functions and input parameters are investigated by Yeong et al (2013). Amiri, Noghondarian, and Safaei (2014) extended the X-bar control chart with variable schemes on the basis of Markov chain approach by considering the Taguchi's quality loss function technique.…”

Section: Introductionmentioning

confidence: 99%

Robust economic-statistical design of X-bar control chart

Safaei

Kazemzadeh

Gan

2015

International Journal of Production Research

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Control charts are developed to monitor the service and production processes. The fact that many processes have uncertain parameters is a barrier to obtain the best design of the control charts. In this paper, economic statistical design (ESD) of the X-bar control chart utilising robust optimisation approach that considers interval estimates of uncertain parameters is investigated. A heuristic algorithm is developed to obtain the robust scheme of the control chart. Robust design for an industrial problem is compared with traditional ESD, and heuristic design. Numerical analyses and simulation study show that the proposed X-bar control chart offers a better approach and more reliable solutions for practitioners.

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“…The economic consequences of this overestimation affect directly the expected loss that can be expressed as function of C pm,True by referring to equations (10) and (11) as below:…”

Section: Effects On the Process Capability Index C Pmmentioning

confidence: 99%

“…This coefficient may be deduced from a replacement/repair cost function A 0 = Loss(T ± d) pertaining to a nonconforming part and expressed as below [10,11],…”

Section: Effects On the Process Capability Index C Pmmentioning

confidence: 99%

Control chart limits based on true process capability with consideration of measurement system error

Amara

Dhahri

Fredj

2016

Int. J. Metrol. Qual. Eng.

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Abstract. Shewhart X and R control charts and process capability indices, proven to be effective tools in statistical process control are widely used under the assumption that the measurement system is free from errors. However, measurement variability is unavoidable and may be evaluated by the measurement system discrimination ratio (DR). This paper investigates the effects of measurement system variability evaluated by DR on the process capability indices C p and C pm , on the expected non conforming units of product per million (ppm), on the expected mean value of the Taguchi loss function (E(Loss)) and on the Shewhart charts properties. It is shown that when measurement system variability is neglected, an overestimation of ppm and underestimation of E(Loss) are induced. Moreover, significant effects of the measurement variability on the control chart properties were made in evidence. Therefore, control charts limits calculation methods based on process real state were developed. An example is provided in order to compare the proposed limits with those traditionally calculated for Shewhart X, R charts.Keywords: process capability indices / expected non conforming units of product per million (ppm) / mean value of Taguchi loss function E(Loss) / measurement system discrimination ratio / Shewhart X, R control chart parameters

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Evaluating the performance of variable scheme X-bar control chart: a Taguchi loss approach

Cited by 11 publications

References 40 publications

Development of U control chart by variable sample size and sampling interval to improve the statistical properties

Development of U control chart by variable sample size and sampling interval to improve the statistical properties

Robust economic-statistical design of X-bar control chart

Control chart limits based on true process capability with consideration of measurement system error

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