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Modeling and prediction of spindle dynamic precision using the Kriging-based response surface method with a novel sampling strategy

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Abstract

The dynamic precision of the spindle directly affects the machining precision and production efficiency of CNC machine tools. Given the complexity of spindle dynamic behaviors under different working conditions, the spindle dynamic precision is difficult to model accurately using theoretical methods. In this paper, a Kriging-based response surface method is proposed for spindle dynamic precision modeling and prediction under all working conditions. Then, a novel sampling strategy is proposed to improve the robustness and efficiency. Specifically, multi-zone Latin hypercube sampling (MZ-LHS) is designed with the consideration of spindle dynamic characteristics for the initial modeling, and an active learning method that combines Kriging and Monte Carlo simulation (AK-MCS) is further improved by parallel processing for the model’s global updating and refinement. The proposed method ensures that the approximation of precision features can be accurately and efficiently fitted under complex working conditions without introducing traditional theoretical rotor dynamics models. The effectiveness of the proposed method is verified by conducting the workload simulation and precision measurement test on a CNC machine tool spindle, and the dynamic precision of the tested spindle is analyzed as a case study.

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Data availability

The data that support the findings of this study are available on request from the corresponding author W. Chen. The data are not publicly available due to them containing information that could compromise research participant privacy.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China [grant number 51975249] and Natural Science Foundation of Chongqing municipality [grant number cstc2021jcyj-msxmX0935].

Funding

National Natural Science Foundation of China [grant number 51975249] and Natural Science Foundation of Chongqing municipality [grant number cstc2021jcyj-msxmX0935].

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Authors and Affiliations

  1. Key Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin University, Changchun, 130025, China

    Chuanhai Chen, Junqi Long, Weizheng Chen, Zhifeng Liu & Jinyan Guo

  2. School of Mechanical and Aerospace Engineering, Jilin University, Changchun, 130025, China

    Chuanhai Chen, Junqi Long, Weizheng Chen, Zhifeng Liu & Jinyan Guo

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  1. Chuanhai Chen
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Correspondence to Weizheng Chen.

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Chen, C., Long, J., Chen, W. et al. Modeling and prediction of spindle dynamic precision using the Kriging-based response surface method with a novel sampling strategy. Nonlinear Dyn 111, 559–579 (2023). https://doi.org/10.1007/s11071-022-07861-1

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