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Ensemble modeling for data fusion in manufacturing process scale-up

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  • Ran Jin
  • Xinwei Deng

Abstract

In modern manufacturing process scale-up, design of experiments is widely used to identify optimal process settings, followed by production runs to validate these process settings. Both experimental data and observational data are collected in the manufacturing process. However, current methodologies often use a single type of data to model the process. This work presents an innovative method to efficiently model a manufacturing process by integrating the two types of data. An ensemble modeling strategy is proposed that utilizes the constrained likelihood approach, where the constraints incorporate the sequential nature and inherent features of the two types of data. It therefore achieves better estimation and prediction than conventional methods. Simulations and a case study in wafer manufacturing are provided to illustrate the merits of the proposed method.

Suggested Citation

  • Ran Jin & Xinwei Deng, 2015. "Ensemble modeling for data fusion in manufacturing process scale-up," IISE Transactions, Taylor & Francis Journals, vol. 47(3), pages 203-214, March.
    • Handle: RePEc:taf:uiiexx:v:47:y:2015:i:3:p:203-214
    DOI: 10.1080/0740817X.2014.916580
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    Cited by:

    1. Shuai Luo & Hongyue Sun & Qingyun Ping & Ran Jin & Zhen He, 2016. "A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects," Energies, MDPI, vol. 9(2), pages 1-27, February.
    2. SungKu Kang & Ran Jin & Xinwei Deng & Ron S. Kenett, 2023. "Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 415-428, February.

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