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Machining accuracy reliability analysis of multi-axis machine tool based on Monte Carlo simulation

Author

Listed:
  • Qiang Cheng

    (Beijing University of Technology
    Huazhong University of Science and Technology)

  • Hongwei Zhao

    (Beijing University of Technology)

  • Yongsheng Zhao

    (Beijing University of Technology)

  • Bingwei Sun

    (Beijing University of Technology)

  • Peihua Gu

    (Shantou University)

Abstract

Although machine tool can meet the specifications while it is new, after a long period of cutting operations, the abrasion of contact surfaces and deformation of structures will degrade the accuracy of machine tool due to the increase of the geometric errors in six freedoms. Therefore, how to maintain its accuracy for quality control of products is of crucial importance to machine tool. In this paper, machining accuracy reliability is defined as the ability to perform its specified machining accuracy under the stated conditions for a given period of time, and a new method to analyze the sensitivity of geometric errors to the machining accuracy reliability is proposed. By applying Multi-body system theory, a comprehensive volumetric model explains how individual geometric errors affect the machining accuracy (the coupling relationship) was established. Based on Monte Carlo mathematic simulation method, the models of the machining accuracy reliability and sensitivity analysis of machine tools were developed. By taking the machining accuracy reliability as a measure of the ability of machine tool and reliability sensitivity as a reference of optimizing the basic parameters of machine tools, an illustrative example of a three-axis machine tool was selected to demonstrate the effectiveness of the proposed method.

Suggested Citation

  • Qiang Cheng & Hongwei Zhao & Yongsheng Zhao & Bingwei Sun & Peihua Gu, 2018. "Machining accuracy reliability analysis of multi-axis machine tool based on Monte Carlo simulation," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 191-209, January.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:1:d:10.1007_s10845-015-1101-1
    DOI: 10.1007/s10845-015-1101-1
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    References listed on IDEAS

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    1. Karadeniz, Halil & ToÄŸan, Vedat & Vrouwenvelder, Ton, 2009. "An integrated reliability-based design optimization of offshore towers," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1510-1516.
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    Cited by:

    1. Ye, Zhenggeng & Cai, Zhiqiang & Zhou, Fuli & Zhao, Jiangbin & Zhang, Pan, 2019. "Reliability analysis for series manufacturing system with imperfect inspection considering the interaction between quality and degradation," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 345-356.
    2. Li, Yao & He, Yihai & Ai, Jun & Wang, Chengcheng & Han, Xiao & Liao, Ruoyu & Yang, Xiuzhen, 2022. "Functional health prognosis approach of multi-station manufacturing system considering coupling operational factors," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    3. 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).
    4. Yang Hui & Xuesong Mei & Gedong Jiang & Fei Zhao & Pengcheng Shen, 2020. "Assembly consistency improvement of straightness error of the linear axis based on the consistency degree and GA-MSVM-I-KM," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1429-1441, August.
    5. 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.
    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. Zhiwei Zhao & Yingguang Li & Changqing Liu & James Gao, 2020. "On-line part deformation prediction based on deep learning," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 561-574, March.

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