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Engineering model-based Bayesian monitoring of ramp-up phase of multistage manufacturing process

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  • Shichang Du
  • Xufeng Yao
  • Delin Huang

Abstract

Process monitoring of full mass production phase of multistage manufacturing processes (MMPs) has been successfully implemented in many applications; however, monitoring of ramp-up phase of MMPs is often more difficult to conduct due to the limited information to establish valid process control parameters (such as mean and variance). This paper focuses on the estimation of the process control parameters used for monitoring scheme design of ramp-up phase of MMPs. An engineering model of variation propagation of an MMP is developed and reconstructed to a linear model, establishing a relationship between the error sources and the variation of product characteristics. Based on the developed linear model, a two-step Bayesian method is proposed to estimate the process control parameters. The performance of the proposed Bayesian method is validated with simulation data and real-world data, and the results demonstrate that the proposed method can effectively estimate process parameters during ramp-up phase of MMP.

Suggested Citation

  • Shichang Du & Xufeng Yao & Delin Huang, 2015. "Engineering model-based Bayesian monitoring of ramp-up phase of multistage manufacturing process," International Journal of Production Research, Taylor & Francis Journals, vol. 53(15), pages 4594-4613, August.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:15:p:4594-4613
    DOI: 10.1080/00207543.2015.1005247
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    Cited by:

    1. Chang-Ho Lee & Dong-Hee Lee & Young-Mok Bae & Seung-Hyun Choi & Ki-Hun Kim & Kwang-Jae Kim, 2022. "Approach to derive golden paths based on machine sequence patterns in multistage manufacturing process," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 167-183, January.
    2. Jinho Kim & Myong K. Jeong & Elsayed A. Elsayed, 2017. "Monitoring multistage processes with autocorrelated observations," International Journal of Production Research, Taylor & Francis Journals, vol. 55(8), pages 2385-2396, April.
    3. Zhou, Chengyu & Fang, Xiaolei, 2023. "A convex two-dimensional variable selection method for the root-cause diagnostics of product defects," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    4. Sangahn Kim & Mehmet Turkoz, 2022. "Bayesian sequential update for monitoring and control of high-dimensional processes," Annals of Operations Research, Springer, vol. 317(2), pages 693-715, October.
    5. H.W. You & Michael B.C. Khoo & P. Castagliola & Liang Qu, 2016. "Optimal exponentially weighted moving average charts with estimated parameters based on median run length and expected median run length," International Journal of Production Research, Taylor & Francis Journals, vol. 54(17), pages 5073-5094, September.

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