A joint monitoring of the process mean and variance with a generally weighted moving average maximum control chart

Chatterjee, Kanu; Koukouvinos, Christos; Lappa, Angeliki; Roupa, Paraskevi

doi:10.1080/03610918.2023.2170415

Cited by 2 publications

(2 citation statements)

References 45 publications

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“…A practical demonstration using a steering knuckle pin example is provided, along with insights into potential future research directions. Chatterjee et al 12 extended the Max-EWMA CC to develop a single Max-GWMA CC for concurrent process mean and variability monitoring. Comparative analysis with Max-EWMA and Max-DEWMA charts revealed the efficiency of the Max-GWMA chart in detecting small shifts in both parameters, demonstrated through practical implementations using real and simulated datasets.…”

Section: Introductionmentioning

confidence: 99%

Memory type Max-EWMA control chart for the Weibull process under the Bayesian theory

Noor-ul-Amin,

Khan,

Iqbal

et al. 2024

Sci Rep

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The simultaneous monitoring of both process mean and dispersion, particularly in normal processes, has garnered significant attention within the field. In this article, we present a new Bayesian Max-EWMA control chart that is intended to track a non-normal process mean and dispersion simultaneously. This is accomplished through the utilization of the inverse response function, especially in cases where the procedure follows a Weibull distribution. We used the average run length (ARL) and the standard deviation of run length (SDRL) to assess the efficacy of our suggested control chart. Next, we contrast our suggested control chart's performance with an already-existing Max-EWMA control chart. Our results show that compared to the control chart under consideration, the proposed control chart exhibits a higher degree of sensitivity. Finally, we present a useful case study centered around the hard-bake process in the semiconductor manufacturing sector to demonstrate the performance of our Bayesian Max-EWMA control chart under different Loss Functions (LFs) for a Weibull process. The case study highlights how flexible the chart is to various situations. Our results offer strong proof of the outstanding ability of the Bayesian Max-EWMA control chart to quickly identify out-of-control signals during the hard-bake procedure. This in turn significantly contributes to the enhancement of process monitoring and quality control.

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Section: Introductionmentioning

confidence: 99%

Memory type Max-EWMA control chart for the Weibull process under the Bayesian theory

Noor-ul-Amin,

Khan,

Iqbal

et al. 2024

Sci Rep

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show abstract

“…Akhtar et al [26] evaluated the EWMA control chart by using log-normal distributions with estimated parameters to monitor process variability. A GWMA maximum chart for joint monitoring was designed by Chatterjee et al [27]. Similarly, Chatterjee et al [28] introduced an efficient control charting structure named double GWMA (DGWMA) for the monitoring of location and dispersion parameters.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Dispersion Monitoring Structures Based on Modified Successive Sampling: Application to Fertilizer Production Process

et al. 2023

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In this era of Industry 4.0, efficient and affordable monitoring solutions are needed for the surveillance of manufacturing/service operations. In general, memory-type control charts outperform memoryless control charts when it comes to determining the changes in location and dispersion parameters of symmetrically distributed processes. Before monitoring the process location, it is essential to monitor the process dispersion, since the latter presumes that the process variance remains stable. In practice, the modified successive sampling (MSS) mechanism is preferred over simple random sampling for its cost-effectiveness and efficiency. This study was designed in order to propose moving average and double moving average control charts based on the MSS mechanism for monitoring the dispersion parameter. The performance of the proposed charts is evaluated using run-length measures, and a comparison is made with an existing control chart based on MSS and repetitive sampling. Furthermore, the application of the designed moving and double moving average charts is demonstrated using a case study related to fertilizer production. It is observed that the proposed double moving average control chart performs better than the other control charts designed under the MSS and repetitive sampling schemes.

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A joint monitoring of the process mean and variance with a generally weighted moving average maximum control chart

Cited by 2 publications

References 45 publications

Memory type Max-EWMA control chart for the Weibull process under the Bayesian theory

Memory type Max-EWMA control chart for the Weibull process under the Bayesian theory

Enhanced Dispersion Monitoring Structures Based on Modified Successive Sampling: Application to Fertilizer Production Process

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