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Modelling the Dynamics of a CNC Spindle for Tool Condition Identification Based on On-Rotor Sensing

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Proceedings of IncoME-VI and TEPEN 2021

Part of the book series: Mechanisms and Machine Science ((Mechan. Machine Science,volume 117))

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Abstract

Cutting tool plays an important role in modern manufacturing industry, however, tool wear is unavoidable during machining which could reduce the efficiency. Aiming at studying an appropriate and efficient tool condition monitoring method to improve the accuracy of finished parts, the roughness of the turned surface, a novel On-Rotor Sensing (ORS) is installed on the rotating workpiece to obtain vibration signals. To get an in-depth understand of the vibration data, a multi-degree-of-freedom (MDOF) system consisted of spindle, chuck and workpiece is established and its multi-mode natural frequency is obtained by finite element model (FEM) method. It is found that the dynamic response of the spindle rotor determines machining accuracy in the turning process and shows that the first several modes in the frequency range within 2000 Hz are the main responses of the system, which can be effectively captured by the ORS. Especially, the spring stiffness is calibrated based on the FEM results and the accuracy of the dynamic modal responses of this model are verified when the mass of the workpiece decreases during the turning process. According to the results, two frequency bands are advocated for ORS based online monitoring of tool wear conditions.

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Acknowledgements

This work is supported by the 2020 Guangdong Province Scientific Research Platform (No. 2020KTSCX188), the Beijing Municipal Science and Technology Project (No. Z201100008320004) and the National Natural Science Foundation of China (No. 2018A030313418).

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Authors and Affiliations

  1. School of Industrial Automation, Beijing Institute of Technology Zhuhai, Zhuhai, 519088, China

    Chun Li & Bing Li

  2. Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, HD1 3DH, UK

    Chun Li, Guojin Feng, Fengshou Gu & Andrew D. Ball

  3. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China

    Dawei Shi

  4. School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing, 100192, China

    Hongjun Wang

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  1. Chun Li
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Corresponding author

Correspondence to Fengshou Gu .

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Editors and Affiliations

  1. School of Mechanical Engineering, Hebei University of Technology, Tianjin, China

    Hao Zhang

  2. School of Mechanical Engineering, Hebei University of Technology, Tianjin, China

    Guojin Feng

  3. School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing, China

    Hongjun Wang

  4. University of Huddersfield, Huddersfield, UK

    Fengshou Gu

  5. University of Manchester, Manchester, UK

    Jyoti K. Sinha

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Li, C. et al. (2023). Modelling the Dynamics of a CNC Spindle for Tool Condition Identification Based on On-Rotor Sensing. In: Zhang, H., Feng, G., Wang, H., Gu, F., Sinha, J.K. (eds) Proceedings of IncoME-VI and TEPEN 2021. Mechanisms and Machine Science, vol 117. Springer, Cham. https://doi.org/10.1007/978-3-030-99075-6_84

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  • DOI: https://doi.org/10.1007/978-3-030-99075-6_84

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-99074-9

  • Online ISBN: 978-3-030-99075-6

  • eBook Packages: EngineeringEngineering (R0)

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