A self-starting procedure for monitoring process quality in multistage manufacturing systems

Zantek, Paul F.; Wright, Gordon P.; Plante, Robert

doi:10.1080/07408170500208354

Cited by 43 publications

(16 citation statements)

References 18 publications

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“…In this section, simulations are performed to compare the performance of the MAR(m)‐based residuals charts with the conventional MAR(1) model‐based ones. Competing monitoring procedures is usually by first designing the procedures to have a common in‐control ARL and then comparing the performance detection power for given process shift . In our study, simulations are also conducted in such a manner.…”

Section: Numerical Comparisonmentioning

confidence: 99%

Effects of Model Accuracy on Residual Control Charts

Zhou

Goh

2015

Qual. Reliab. Engng. Int.

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Residual control charts are acknowledged to be effective tools for statistical process control of multistage processes. In these monitoring procedures, the models on the stage-wise correlation should be first derived before the control charts are implemented. Therefore, the monitoring performance is inevitably affected by the model fitting scheme. Most of the previous works are under the assumption that the derived models represent the process behavior perfectly. Far less is known about the effects of the model inaccuracy on the monitoring performance. To investigate the effects of the underlying models on the monitoring performance, residual control charts based on two different modeling schemes are compared in this paper. The results indicate that the charting performance is correlated with the model fitting schemes. That is, a more accurate model will significantly increase the detection power and decrease the false alarm rate as well.

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Section: Numerical Comparisonmentioning

confidence: 99%

Effects of Model Accuracy on Residual Control Charts

Zhou

Goh

2015

Qual. Reliab. Engng. Int.

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“…It should be highlighted that the chart we used here is different from that used by Zhang (1985), Hawkins (1991Hawkins ( , 1993 and Zantek et al (2006). They respectively used ā X chart, a CUSUM chart and a T 2 chart to monitor the residuals because they were mainly interested in mean shifts, whereas in our study we select the S chart for the purpose of monitoring variability/variance changes.…”

Section: The Regression-adjusted Variability Monitoring Proceduresmentioning

confidence: 99%

“…However, little work has been reported on this topic. In most cases, for every QC j, people just monitor its residual that is resulted from the adjustment of all its preceding QCs (e.g., Hawkins (1993) and Zantek et al (2006)) without the pre-examination of the subset of QCs that really influences j. Thus, it is necessary to investigate the possible advantages or disadvantages if a regressor selection procedure, which is usually an indispensable part in the application of regression, is added into the regression adjustment method.…”

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confidence: 99%

Variability monitoring of multistage manufacturing processes using regression adjustment methods

Zeng

Zhou

2007

IIE Transactions

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The recent trends in manufacturing toward modularity and flexibility result in complex multistage manufacturing processes that consist of many interrelated workstations. In such processes, it is highly desirable to differentiate between local and propagated variations, and implement process variability monitoring and reduction. In this paper, attention is focused on the properties of a widely used regression-adjustment-based method in the monitoring of variation propagation in multistage manufacturing processes. Particularly, the impacts of measurement errors and regressor selection on the monitoring scheme are investigated, and conclusions which can help guide the use of this method are summarized. Numerical examples are also presented to validate the analysis.

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“…Lawless et al (1999) used AR(1) to study variation in attribute of products that move through multistage manufacturing processes by tracking and measuring individual parts as they pass through multiple stages. Considering quality linkage across stages, Zantek et al (2002Zantek et al ( , 2006) established a simultaneous-equation model to measure the impact of each stage's performance on the output quality of subsequent stages, including the quality of the final product. Undey and Cinar (2002) proposed a multivariate statistical process monitoring (MSPM) framework of multistage, multiphase processes for chemical process industry, in which local model and super-level model were used to monitor local variable and identify localised faults in process stages respectively.…”

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confidence: 99%

Modelling of machining error flow based on form features for multistage processes

Liu

Jiang

2009

International Journal of Computer Integrated Manufacturing

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The machining quality of a workpiece is determined by many machining process elements, such as the machine tool, fixturing, and process planning, etc. It is especially observed that there is a propagation chain of quality attributes in the multistage machining processes (MMPs). Owing to the propagation chain of quality attributes, the machining quality of the workpiece at a single stage is determined not only by the machining elements of the current stage, but also by the quality attributes of its previous stages. In order to describe the propagation chain, a form-feature-based quality control model for MMPs is proposed. Based on this, a linear error propagation model is established by the use of a rigid-body kinematics method, which can explicitly describe the mapping relationship between machining errors of quality attributes and machining process elements. In order to have some comparison with the error propagation model, the above linear regression model has been built to enable correlated quality attributes to be determined with this error propagation model. A workpiece has been used to show that the error propagation model can effectively describe the propagation chain of the quality attributes.

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A self-starting procedure for monitoring process quality in multistage manufacturing systems

Cited by 43 publications

References 18 publications

Effects of Model Accuracy on Residual Control Charts

Effects of Model Accuracy on Residual Control Charts

Variability monitoring of multistage manufacturing processes using regression adjustment methods

Modelling of machining error flow based on form features for multistage processes

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