Using cause selecting control charts to monitor dependent process stages with attributes data

Yang, Su‐Fen; Yeh, Jin-Tyan

doi:10.1016/j.eswa.2010.07.018

Cited by 22 publications

(6 citation statements)

References 11 publications

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“…This study uses a chart for Individuals (I) and Moving Range (MR) (I‐MR chart) to determine the characteristics of the process for the deposition of AGZO films. The I chart shows the individual values for each estimate for the fabrication process and the MR chart shows changes in the process variability . Figure shows the control charts that are used to monitor the quality of process for the deposition of AGZO films for the individual value and the moving range (I‐MR chart).…”

Section: Resultsmentioning

confidence: 99%

Fabrication of Al and Ga Co‐Doped ZnO Films on Flexible Substrates and Control Charts Monitor Deposition Process

Lin

2019

Physica Status Solidi (a)

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doped zinc oxide (AGZO) films are deposited onto flexible poly (ethylene terephthalate) (PET) substrates by radio frequency magnetron sputtering. Before the AGZO film coating, oxygen plasma etching is used to modify the surface of the PET substrate. The results show that the substrate is very hydrophilic. This study determines the effect of the deposition parameters on the structural, morphological, and optoelectronic performances of AGZO films. Based on the gray Taguchi analysis for the AGZO film coatings show that the electrical resistivity is decreased to 6.30 Â 10 À3 Ω cm and the optical transmittance is increased to 81.0%. The XRD diffraction and TEM images show that the AGZO films exhibit a hexagonal ZnO wurtzite structure. A comparison of the PET substrates that do or do not undergo plasma pretreatment, shows that the AGZO films coating there is a 4.7% improvement in elastic recovery, a 13.65% improvement in hardness H, a 1.69% improvement in the elastic modulus E, a 12.15% improvement in in H/E, and a 42.06% improvement in H 3 /E 2 . Shewhart control charts are used to monitor the deposition processes for the AGZO films. Finding shown that the deposition parameters for the films and process performance are in control and stable.

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Section: Resultsmentioning

confidence: 99%

Fabrication of Al and Ga Co‐Doped ZnO Films on Flexible Substrates and Control Charts Monitor Deposition Process

Lin

2019

Physica Status Solidi (a)

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“…Other examples include economic design of control chart for monitoring multistage processes, 22,23 model-based control chart for monitoring multistage processes with autoregressive and correlated data, 24 multistage processes monitoring when observations are autocorrelated, 25,26 using variable sample size control charts for multistage processes, 27 using the variable sampling interval scheme to monitoring and diagnosing dependent process steps, 28 using generalized-linear model-based control charts for correlated gamma-distributed data, 29 a monitoring scheme for detecting a mean shift in a multistage manufacturing process for a gamma distributed response, 2 monitoring of multistage processes with binary outputs, 7 monitoring of multistage processes for detecting covariance matrix changes, 30 and monitoring dependent process stages with attributes data. 31 In addition to the aforementioned studies, several research have dealt with the effect of outliers or contamination on multistage process monitoring plans. Jearkpaporn et al 32 presented a robust approach for monitoring a multistage process where data follow the gamma distribution.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

A robust control chart for simple linear profiles in two‐stage processes

Hassanvand

Samimi

Shahriari

2019

Quality & Reliability Eng

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In many manufacturing systems, the final product is the result of several dependent stages. In particular, in multistage processes, the quality characteristics of downstream stages are influenced by those in the earlier stages (upstream). This property is referred to as the cascade property, which, if disregarded in process monitoring, may bring about misleading results for the subsequent fault diagnosis. Considering the relationships among consecutive stages of the process, the U statistic is the most widely used method for monitoring multistage processes. Using this method, our paper deals with monitoring a two‐stage process where quality characteristics are represented as simple linear profiles. To guard against the detrimental effect of contaminated data in the phase I of statistical process control, two well‐known robust M‐estimators, including Huber's and bi‐square, are employed for estimation of the process parameters. Under different degrees of autocorrelation across stages of the process and also for different contamination rates, the performances of the proposed methods are compared with that of the conventional least‐square method. From the viewpoint of estimation performance measures, including unbiasedness and efficiency, along with the capability of the control chart in identifying the true source of variation, extensive simulation results reveal that robust estimators outperform the traditional method in a two‐stage process. Meanwhile, it should be noted when there is contamination only in the first stage of the process, the least‐square method performs slightly better.

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“…investigated the problem of multistage process monitoring of binary outputs. Yang and Yeh used CSC charts to monitor dependent process stages with attribute data. They compared the performance of their proposed CSC chart with the ones of Shewhart attribute charts as well as a binomial bivariate control region.…”

Section: Introductionmentioning

confidence: 99%

On the Monitoring of Linear Profiles in Multistage Processes

Ghahyazi

Niaki

Soleimani

2013

Quality & Reliability Eng

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In most modern manufacturing systems, products are often the output of several correlated stages. Nevertheless, quality of a product or process in both single and multistage processes is usually expressed by a single quality characteristic, two or more characteristics, or profiles. Although there are many studies in univariate and multivariate‐multistage process monitoring, fewer works focus on profile monitoring of multistage processes. This paper addresses the problem of monitoring a simple linear profile that is going through a multistage process in phase II. Using a first‐order autoregressive correlation model, the relationship between the stages is first modeled. Then, the cascade effect of multistage processes on the performance of T2 control chart is studied. We show that the cascade property has a significant impact on the performances of the chart in downstream stages. Next, a U statistic is used to eliminate the cascade effect, and the control scheme is modified accordingly. Simulation studies reveal that the modified control chart performs well. Copyright © 2013 John Wiley & Sons, Ltd.

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Using cause selecting control charts to monitor dependent process stages with attributes data

Cited by 22 publications

References 11 publications

Fabrication of Al and Ga Co‐Doped ZnO Films on Flexible Substrates and Control Charts Monitor Deposition Process

Fabrication of Al and Ga Co‐Doped ZnO Films on Flexible Substrates and Control Charts Monitor Deposition Process

A robust control chart for simple linear profiles in two‐stage processes

On the Monitoring of Linear Profiles in Multistage Processes

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