International Journal of Quality Engineering and Technology
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International Journal of Quality Engineering and Technology (3 papers in press)
DESIGN OF EWMA CONTROL CHART FOR MONITORING TRANSFORMED RAYLEIGH DISTRIBUTED DATA by Olatunde Adeoti Abstract: Monitoring statistical process for the detection of assignable causes of variation is based on the assumption that the process characteristic follow the normal distribution. But, in practice, this is often not the case as process characteristic seldom follow the non-normal distribution. This paper design a new control chart to monitor quality characteristic that follow the non-normal distribution. The proposed control chart based on the EWMA statistic is constructed after transforming the Rayleigh distributed data to approximate normal using the power transformation method. The ARL and SDRL values of the proposed control chart are evaluated for different shift sizes. The performance of the proposed chart is compared with the recent CUSUM chart for transformed Rayleigh distributed data. The study shows that the proposed chart outperforms the recent CUSUM control chart for transformed Rayleigh data. Real-life and simulated dataset to illustrate the design and applications of the proposed control chart is given. Keywords: Control chart; Transformed Rayleigh data; EWMA; ARL; Power transformation.
Joint optimization of production run length and maintenance policy for an imperfect process with multiple correlated quality characteristics by Ali Salmasnia, Maryam Kaveie Abstract: The earliest economic production quantity models assumed that the manufacturing process and the quality of produced items are perfect. While in a real situation, non-conforming products are fabricated and machine failure happens. Hence, the production systems are increasingly engaged in the improvement of machines availability and products quality. In this regard, this paper presents an integrated production and maintenance planning model under monitoring multiple quality characteristics. To adapt to the real production conditions, it is considered that quality characteristics are correlated. Furthermore, to improve the power of process monitoring, a shewhart control chart is designed by considering both economic and statistical criteria. Due to the complexity of the problem, the particle swarm optimization algorithm is employed to optimize the expected total cost per time unit, subject to statistical quality constraints. Here, an industrial example is given to show applicability of the presented mathematical programming. Furthermore, to demonstrate the validation and effectiveness of the suggested approach, a comparative study is presented. It confirms that the integration of production planning, maintenance policy, and statistical process monitoring leads to a significant increase in the cost savings. Keywords: Production run length; maintenance policy; statistical process monitoring; multiple-quality characteristics.
Design and Implementation of ARL-unbiased CCCr-chart for Monitoring High-yield Processes by Nirpeksh Kumar, Ranjeet Kumar Singh Abstract: In order to overcome the shortcomings of the conventional charts such as p-, c-, u-chart in monitoring the high-quality processes with low fraction nonconforming, the cumulative counts of conforming (CCC) charts are recommended in Statistical Process Control (SPC) literature. To improve further their ability of detection early shifts in the fraction nonconforming, the CCC_r-charts are proposed considering the cumulative count of conforming items up to the r-th nonconforming one. But the CCC_r-charts perform poorly in detection of small downward shifts in the fraction nonconforming because of their undesirable ARL-biased property. This results in the larger out-of-control (OOC) average run length (ARL) values than the in-control ARL value for some values of the fraction nonconforming. In this paper, we eliminate the ARL-biasedness property and propose the ARL-unbiased CCC_r-charts using the notion of uniformly most powerful unbiased (UMPU) test to ensure that a user will get an OOC signal more quickly than a false alarm for the shifts in both upward and downward directions. The performance of the proposed chart is also compared with the existing ARL-unbiased CCC chart and it is found that the former has an improved ability of detecting shifts in the fraction nonconforming over the latter. An illustrative example is given and a summary and conclusions are offered. Keywords: average run length; ARL-unbiased; control chart; fraction nonconforming; geometric distribution; high-yield processes; in-control and out-of-control performance; uniformly most powerful unbiased test.