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Real-time machining data application and service based on IMT digital twin

Author

Listed:
  • Xin Tong

    (Beihang University)

  • Qiang Liu

    (Beihang University
    Beijing Engineering Technological Research Center of High-efficient and Green CNC Machining Process and Equipment)

  • Shiwei Pi

    (Beihang University)

  • Yao Xiao

    (Beihang University)

Abstract

With the development of manufacturing, machining data applications are becoming a key technological component of enhancing the intelligence of manufacturing. The new generation of machine tools should be digitalized, highly efficient, network-accessible and intelligent. An intelligent machine tool (IMT) driven by the digital twin provides a superior solution for the development of intelligent manufacturing. In this paper, a real-time machining data application and service based on IMT digital twin is presented. Multisensor fusion technology is adopted for real-time data acquisition and processing. Data transmission and storage are completed using the MTConnect protocol and components. Multiple forms of HMIs and applications are developed for data visualization and analysis in digital twin, including the machining trajectory, machining status and energy consumption. An IMT digital twin model is established with the aim of further data analysis and optimization, such as the machine tool dynamics, contour error estimation and compensation. Examples of the IMT digital twin application are presented to prove that the development method of the IMT digital twin is effective and feasible. The perspective development of machining data analysis and service is also discussed.

Suggested Citation

  • Xin Tong & Qiang Liu & Shiwei Pi & Yao Xiao, 2020. "Real-time machining data application and service based on IMT digital twin," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1113-1132, June.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:5:d:10.1007_s10845-019-01500-0
    DOI: 10.1007/s10845-019-01500-0
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    References listed on IDEAS

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    1. Changqing Liu & Yingguang Li & Guanyan Zhou & Weiming Shen, 2018. "A sensor fusion and support vector machine based approach for recognition of complex machining conditions," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1739-1752, December.
    2. Sanjay Jain & Guodong Shao & Seung-Jun Shin, 2017. "Manufacturing data analytics using a virtual factory representation," International Journal of Production Research, Taylor & Francis Journals, vol. 55(18), pages 5450-5464, September.
    3. Tangbin Xia & Lifeng Xi, 2019. "Manufacturing paradigm-oriented PHM methodologies for cyber-physical systems," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1659-1672, April.
    4. Wei Ji & Shubin Yin & Lihui Wang, 2019. "A big data analytics based machining optimisation approach," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1483-1495, March.
    5. Fei Tao & Fangyuan Sui & Ang Liu & Qinglin Qi & Meng Zhang & Boyang Song & Zirong Guo & Stephen C.-Y. Lu & A. Y. C. Nee, 2019. "Digital twin-driven product design framework," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3935-3953, June.
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    Citations

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    Cited by:

    1. Georgios Falekas & Athanasios Karlis, 2021. "Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects," Energies, MDPI, vol. 14(18), pages 1-26, September.
    2. PengYu Wang & Wen-An Yang & YouPeng You, 2023. "A cyber-physical prototype system in augmented reality using RGB-D camera for CNC machining simulation," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3637-3658, December.
    3. Ayman AboElHassan & Soumaya Yacout, 2023. "A digital shadow framework using distributed system concepts," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3579-3598, December.
    4. Zengya Zhao & Sibao Wang & Zehua Wang & Shilong Wang & Chi Ma & Bo Yang, 2022. "Surface roughness stabilization method based on digital twin-driven machining parameters self-adaption adjustment: a case study in five-axis machining," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 943-952, April.
    5. Ahmed Ktari & Mohamed El Mansori, 2022. "Digital twin of functional gating system in 3D printed molds for sand casting using a neural network," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 897-909, March.
    6. Chi Ma & Hongquan Gui & Jialan Liu, 2023. "Self learning-empowered thermal error control method of precision machine tools based on digital twin," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 695-717, February.
    7. Benjamin Lutz & Dominik Kisskalt & Andreas Mayr & Daniel Regulin & Matteo Pantano & Jörg Franke, 2021. "In-situ identification of material batches using machine learning for machining operations," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1485-1495, June.
    8. Aniket Nagargoje & Pavan Kumar Kankar & Prashant Kumar Jain & Puneet Tandon, 2023. "Application of artificial intelligence techniques in incremental forming: a state-of-the-art review," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 985-1002, March.

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