Statistical Merits and Economic Evaluation of T2 Control Charts with the VSSC Scheme

Seif, Asghar; Moghadam, M. Bameni; Faraz, Alireza; Heuchenne, Cédric

doi:10.1007/s13369-011-0124-y

Cited by 12 publications

(4 citation statements)

References 30 publications

(47 reference statements)

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“…With the complexity of products, the need for simultaneous monitoring of multiple quality characteristics appeared and caused the first economic design of the Hotelling T 2 control charts developed by Montgomery and Klatt (1972). In most of the research works such as Heikes, Montgomery, and Yeung (1974), Yang and Rahim (2006), Chen, Hsieh, and Chang (2007), Seif, Moghadam, Faraz, and Heuchenne (2011), Bahiraee and Raissi (2014), Faraz, Heuchenne, Saniga, and Costa (2014)the economic design of T 2 control chart is presented with single assignable cause Jolayemi and Berrettoni (1989) generalized Duncan (1971) cost model, and produce economic design of T 2 control chart with multiple assignable causes.…”

Section: Introductionmentioning

confidence: 99%

Integration of Multivariate Loss Function Approach in the Hotelling’s Charts under Banerjee-Rahim (1988) Weibull Shock Models

Naderi¹,

Seif²,

Moghadam³

2021

AJS

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A proper monitoring of stochastic systems is the control charts of statistical process control and drift in characteristics of output may be due to one or several assignable causes. Although many research works have been done on the economic design of control charts with single assignable cause, the economic statistical design of T^2 control chart under Weibull shock model with multiple assignable causes and considering multivariate Taguchi loss function has not been presented yet. Using Taguchi loss function in the concept of quality control charts with economic and economic statistical design leads to better decisions in the industry. Based on the optimization of the average cost per unit of time and taking into account the different combination values of Weibull distribution parameters, optimal design values ??of sample size, sampling interval and control limit coefficient were derived and calculated. Then the cost models under non-uniform and uniform sampling scheme were compared. The results revealed that the model under multiple assignable causes with Taguchi loss function has a lower cost than single assignable cause model and integrated model with non-uniform sampling has a lower cost than that with uniform sampling.

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Section: Introductionmentioning

confidence: 99%

Integration of Multivariate Loss Function Approach in the Hotelling’s Charts under Banerjee-Rahim (1988) Weibull Shock Models

Naderi¹,

Seif²,

Moghadam³

2021

AJS

Self Cite

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“…Rapid developments in data-acquisition in industry have led to increased interest in the joint monitoring of several related process parameters [1]. As a result, multivariate process control (MPC) methodology, in which several related process parameters are jointly monitored [2], is one of the most rapidly developing areas in statistical process control (SPC). Several MPC tools that use the relationships among the variables to provide efficient monitoring schemes for identifying any changes in the quality of the products have been proposed.…”

Section: Introductionmentioning

confidence: 99%

A Multivariate Homogeneously Weighted Moving Average Control Chart

et al. 2019

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This paper presents a multivariate homogeneously weighted moving average (MHWMA) control chart for monitoring a process mean vector. The MHWMA control chart statistic gives a specific weight to the current observation, and the remaining weight is evenly distributed among the previous observations. We present the design procedure and compare the average run length (ARL) performance of the proposed chart with multivariate Chi-square, multivariate EWMA, and multivariate cumulative sum control charts. The ARL comparison indicates superior performance of the MHWMA chart over its competitors, particularly for the detection of small shifts in the process mean vector. Examples are also provided to show the application of the proposed chart. INDEX TERMS Average run length, control chart, manufacturing process, quality control, statistical process control.

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“…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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Statistical Merits and Economic Evaluation of T2 Control Charts with the VSSC Scheme

Cited by 12 publications

References 30 publications

Integration of Multivariate Loss Function Approach in the Hotelling’s Charts under Banerjee-Rahim (1988) Weibull Shock Models

Integration of Multivariate Loss Function Approach in the Hotelling’s Charts under Banerjee-Rahim (1988) Weibull Shock Models

A Multivariate Homogeneously Weighted Moving Average Control Chart

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

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