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Simulating a virtual machining model in an agent-based model for advanced analytics

Author

Listed:
  • David Lechevalier

    (Université de Bourgogne)

  • Seung-Jun Shin

    (Pukyong National University)

  • Sudarsan Rachuri

    (Department of Energy)

  • Sebti Foufou

    (Université de Bourgogne
    New York University Abu Dhabi)

  • Y. Tina Lee

    (National Institute of Standards and Technology)

  • Abdelaziz Bouras

    (Qatar University)

Abstract

Monitoring the performance of manufacturing equipment is critical to ensure the efficiency of manufacturing processes. Machine-monitoring data allows measuring manufacturing equipment efficiency. However, acquiring real and useful machine-monitoring data is expensive and time consuming. An alternative method of getting data is to generate machine-monitoring data using simulation. The simulation data mimic operations and operational failure. In addition, the data can also be used to fill in real data sets with missing values from real-time data collection. The mimicking of real manufacturing systems in computer-based systems is called “virtual manufacturing”. The computer-based systems execute the manufacturing system models that represent real manufacturing systems. In this paper, we introduce a virtual machining model of milling operations. We developed a prototype virtual machining model that represents 3-axis milling operations. This model is a digital mock-up of a real milling machine; it can generate machine-monitoring data from a process plan. The prototype model provides energy consumption data based on physics-based equations. The model uses the standard interfaces of Step-compliant data interface for Numeric Controls and MTConnect to represent process plan and machine-monitoring data, respectively. With machine-monitoring data for a given process plan, manufacturing engineers can anticipate the impact of a modification in their actual manufacturing systems. This paper describes also how the virtual machining model is integrated into an agent-based model in a simulation environment. While facilitating the use of the virtual machining model, the agent-based model also contributes to the generation of more complex manufacturing system models, such as a virtual shop-floor model. The paper describes initial building steps towards a shop-floor model. Aggregating the data generated during the execution of a virtual shop-floor model allows one to take advantage of data analytics techniques to predict performance at the shop-floor level.

Suggested Citation

  • David Lechevalier & Seung-Jun Shin & Sudarsan Rachuri & Sebti Foufou & Y. Tina Lee & Abdelaziz Bouras, 2019. "Simulating a virtual machining model in an agent-based model for advanced analytics," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1937-1955, April.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:4:d:10.1007_s10845-017-1363-x
    DOI: 10.1007/s10845-017-1363-x
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    References listed on IDEAS

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    1. Andrew Kusiak, 2017. "Smart manufacturing must embrace big data," Nature, Nature, vol. 544(7648), pages 23-25, April.
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    Cited by:

    1. Li, Hongcheng & Yang, Dan & Cao, Huajun & Ge, Weiwei & Chen, Erheng & Wen, Xuanhao & Li, Chongbo, 2022. "Data-driven hybrid petri-net based energy consumption behaviour modelling for digital twin of energy-efficient manufacturing system," Energy, Elsevier, vol. 239(PC).

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