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Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective

Author

Listed:
  • SungKu Kang

    (Virginia Polytechnic Institute and State University)

  • Ran Jin

    (Virginia Polytechnic Institute and State University)

  • Xinwei Deng

    (Virginia Polytechnic Institute and State University)

  • Ron S. Kenett

    (Israel and University of Turin)

Abstract

In Industry 4.0, smart manufacturing is facing its next stage, cybermanufacturing, founded upon advanced communication, computation, and control infrastructure. Cybermanufacturing will unleash the potential of multi-modal manufacturing data, and provide a new perspective called computation service, as a part of service-oriented architecture (SOA), where on-demand computation requests throughout manufacturing operations are seamlessly satisfied by data analytics and machine learning. However, the complexity of information technology infrastructure leads to fundamental challenges in modeling and analysis under cybermanufacturing, ranging from information-poor datasets to a lack of reproducibility of analytical studies. Nevertheless, existing reviews have focused on the overall architecture of cybermanufacturing/SOA or its technical components (e.g., communication protocol), rather than the potential bottleneck of computation service with respect to modeling and analysis. In this paper, we review the fundamental challenges with respect to modeling and analysis in cybermanufacturing. Then, we introduce the existing efforts in computation pipeline recommendation, which aims at identifying an optimal sequence of method options for data analytics/machine learning without time-consuming trial-and-error. We envision computation pipeline recommendation as a promising research field to address the fundamental challenges in cybermanufacturing. We also expect that computation pipeline recommendation can be a driving force to flexible and resilient manufacturing operations in the post-COVID-19 industry.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:2:d:10.1007_s10845-021-01817-9
    DOI: 10.1007/s10845-021-01817-9
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    References listed on IDEAS

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    1. 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.
    2. Raymond K. W. Wong & Curtis B. Storlie & Thomas C. M. Lee, 2017. "A frequentist approach to computer model calibration," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 635-648, March.
    3. Wancheng Feng & Chen Wang & Zuo-Jun Max Shen, 2017. "Process flexibility design in heterogeneous and unbalanced networks: A stochastic programming approach," IISE Transactions, Taylor & Francis Journals, vol. 49(8), pages 781-799, August.
    4. Kendrik Yan Hong Lim & Pai Zheng & Chun-Hsien Chen, 2020. "A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1313-1337, August.
    5. Juan Pablo Usuga Cadavid & Samir Lamouri & Bernard Grabot & Robert Pellerin & Arnaud Fortin, 2020. "Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1531-1558, August.
    6. Higdon, Dave & Gattiker, James & Williams, Brian & Rightley, Maria, 2008. "Computer Model Calibration Using High-Dimensional Output," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 570-583, June.
    7. Jeremy Oakley, 2002. "Bayesian inference for the uncertainty distribution of computer model outputs," Biometrika, Biometrika Trust, vol. 89(4), pages 769-784, December.
    8. Maurizio Bevilacqua & Eleonora Bottani & Filippo Emanuele Ciarapica & Francesco Costantino & Luciano Di Donato & Alessandra Ferraro & Giovanni Mazzuto & Andrea Monteriù & Giorgia Nardini & Marco Orten, 2020. "Digital Twin Reference Model Development to Prevent Operators’ Risk in Process Plants," Sustainability, MDPI, vol. 12(3), pages 1-17, February.
    9. Hao Yan & Kamran Paynabar & Jianjun Shi, 2020. "AKM2D: An adaptive framework for online sensing and anomaly quantification," IISE Transactions, Taylor & Francis Journals, vol. 52(9), pages 1032-1046, September.
    10. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
    11. Samer Jaloudi, 2019. "Communication Protocols of an Industrial Internet of Things Environment: A Comparative Study," Future Internet, MDPI, vol. 11(3), pages 1-18, March.
    12. Dmitry Ivanov & Alexandre Dolgui, 2020. "Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak," International Journal of Production Research, Taylor & Francis Journals, vol. 58(10), pages 2904-2915, May.
    13. Loayza,Norman V. & Pennings,Steven Michael, 2020. "Macroeconomic Policy in the Time of COVID-19 : A Primer for Developing Countries," Research and Policy Briefs 147291, The World Bank.
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