On the advantages of economically designed the Hotelling’s T2 control chart with variable sample sizes and sampling intervals

Faraz, Alireza; Kazemzadeh, Reza Baradaran; Parsian, Ahmad; Moghadam, M. Bameni

doi:10.1007/s11135-010-9325-9

Cited by 11 publications

(3 citation statements)

References 17 publications

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“…Most recent research focusing on VSI control charts can be seen in ref. [15–18]. However, we found that although there are some research indicating the benefit of using VSI strategy for

n p

and

\overset{X}{true}

control charts, there is still no research on its impact on the

n p_{x} - \bar{X}

control chart.…”

Section: Introductionmentioning

confidence: 84%

Integration of quadratic loss function approach in the economic design of VSI control chart with combined attribute‐variable inspection

Wan

Mei

2020

Quality & Reliability Eng

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This paper proposes a variable sampling interval npx−X¯ control chart for monitoring the process mean using combined attribute‐variable inspection. An economic design of the proposed chart using Taguchi's quadratic loss function is presented. The proposed model minimizes the total expected cost per unit time while minimizing out‐of‐control average time‐to‐signal and maintaining reasonable in‐control average time‐to‐signal. The numerical results indicate that, compared with the corresponding fixed sampling interval control chart scheme, the proposed control chart yields better performance in terms of both shorter time to signal a process shift and less total expected cost per unit time. Sensitivity analyses of the cost model are also carried out by using a fractional factorial experimental design.

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“…Most recent research focusing on VSI control charts can be seen in ref. [15–18]. However, we found that although there are some research indicating the benefit of using VSI strategy for

n p

and

\overset{X}{true}

control charts, there is still no research on its impact on the

n p_{x} - \bar{X}

control chart.…”

Section: Introductionmentioning

confidence: 84%

Integration of quadratic loss function approach in the economic design of VSI control chart with combined attribute‐variable inspection

Wan

Mei

2020

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…In the control charts field, many approaches, generalizations and modifications have been proposed. For example, the adaptive control charts consider a variable sample size or a variable sample size frequency, or both (Mahadik and Shirke, 2011;Seif et al, 2011;Faraz et al, 2012). In control charts with sequential sampling, the sampling is made in steps according to the location of the statistic values (Khoo et al, 2010;Irianto and Juliani, 2010;Costa and Machado, 2011).…”

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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“…Desde entonces, se han propuesto nuevos enfoques, generalizaciones y modificaciones. Por ejemplo, las cartas de control adaptativas consideran tamaños de muestra variables o frecuencia variable de muestreo o ambos (Mahadik and Shirke 2011;Seif et al 2011;Faraz et al 2012). En las cartas de control con muestreo secuencial, el muestreo en fases según la localización de los valores del estadís-tico de prueba (Irianto and Juliani 2010;Khoo et al 2010;Costa and Machado 2011).…”

Section: Introductionunclassified

Carta de control D^2 para procesos multinomiales: estudio de desempeño

Ramírez-Méndez¹,

Cantú-Sifuentes

2017

IIT

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ResumenEl control estadístico de procesos es un conjunto de técnicas donde su empleo puede contribuir a mejorar la calidad de cualquier proceso industrial. En particular, las cartas de control o monitoreo son herramientas poderosas para detectar deterioro en la calidad de un proceso. Sin embargo, cuando las variables o características de calidad del proceso son discretas, el uso de una carta de control puede implicar altos costos de muestreo. Aunque muchos esquemas existentes no restringen el tamaño de muestra a un valor determinado, se sabe que la elección del tamaño de muestra tiene un efecto en el desempeño de una carta de control: usar un tamaño de muestra grande implica mejor precisión en la estimación, por lo tanto, la carta tendrá mejor desempeño, y viceversa, usar un tamaño de muestra pequeño implica peor precisión, por lo tanto, la carta tendrá un desempeño pobre. En este artículo se estudia el desempeño de la carta de control D 2 para variables discretas y se analiza el efecto que tiene el tamaño de muestra. Adicionalmente, se obtienen diseños óptimos de la carta. La medida de desempeño que se emplea es la longitud promedio de corrida (ARL, Average Run Length).Descriptores: ARL, cartas de control, multinomial, tamaño de muestra. AbstractThe application of the Statistical Process Control or SPC is useful to improve any industrial process. For example, the control charts are powerful tools to detect quality deterioration. However, high sampling costs can be involved when the quality characteristics are discrete. It is known that the sample size affects the control charts performance: a large sample size implies high performance and vice versa, a small sample implies worst performance. In this article, the performance of D 2 control chart is studied and the sample size effect is analyzed. Additionally, optimal designs are obtained. The analysis is made in ARL terms.

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On the advantages of economically designed the Hotelling’s T2 control chart with variable sample sizes and sampling intervals

Abstract: Hotelling’s T 2 control chart, Adjusted average time to signal (AATS), Variable sample size and sampling interval (VSSVSI) scheme, Economic design, Markov chain, Genetic algorithm (GA),

Cited by 11 publications

References 17 publications

Integration of quadratic loss function approach in the economic design of VSI control chart with combined attribute‐variable inspection

Integration of quadratic loss function approach in the economic design of VSI control chart with combined attribute‐variable inspection

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

Carta de control D^2 para procesos multinomiales: estudio de desempeño

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