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A geometric error budget method to improve machining accuracy reliability of multi-axis machine tools

Author

Listed:
  • Ziling Zhang

    (Beijing University of Technology)

  • Ligang Cai

    (Beijing University of Technology)

  • Qiang Cheng

    (Beijing University of Technology
    Central South University)

  • Zhifeng Liu

    (Beijing University of Technology)

  • Peihua Gu

    (Shantou University)

Abstract

Machining accuracy reliability is considered to be one of the most important indexes in the process of performance evaluation and optimization design of the machine tools. Geometric errors, thermal errors and tool wear are the main factors to affect the machining accuracy and so affect the machining accuracy reliability of machine tools. This paper proposed a geometric error budget method that simultaneously considers geometric errors, thermal errors and tool wear to improve the machining accuracy reliability of machine tools. Homogeneous transformation matrices, neural fuzzy control theory and a tool wear predictive approach were employed to develop a comprehensive error model, which shows the influence of the geometric, thermal errors and tool wear to the machining accuracy of a machine tool. Based on Rackwite–Fiessler and Advanced First Order and Second Moment, a reliability model and a sensitivity model were put forward, which can deal with the errors of a machine tool drawn from any distribution. Then, a geometric error budget method of multi-axis NC machine tool was developed and formed into a mathematical model. In such method, the minimum cost of machine tool was the optimization objective, the reliability of the machining accuracy was the constraint, and the sensitivity was to identify the geometric errors to be optimized. An example conducted on a five-axis NC machine tool was used to explain and validate the proposed method.

Suggested Citation

  • Ziling Zhang & Ligang Cai & Qiang Cheng & Zhifeng Liu & Peihua Gu, 2019. "A geometric error budget method to improve machining accuracy reliability of multi-axis machine tools," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 495-519, February.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:2:d:10.1007_s10845-016-1260-8
    DOI: 10.1007/s10845-016-1260-8
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    Citations

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    Cited by:

    1. Antoni Świć & Arkadiusz Gola & Łukasz Sobaszek & Natalia Šmidová, 2021. "A thermo-mechanical machining method for improving the accuracy and stability of the geometric shape of long low-rigidity shafts," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1939-1951, October.
    2. Ye, Zhenggeng & Yang, Hui & Cai, Zhiqiang & Si, Shubin & Zhou, Fuli, 2021. "Performance evaluation of serial-parallel manufacturing systems based on the impact of heterogeneous feedstocks on machine degradation," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    3. Antonio Caputi & Davide Russo, 2021. "The optimization of the control logic of a redundant six axis milling machine," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1441-1453, June.
    4. 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.

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