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A gear machining error prediction method based on adaptive Gaussian mixture regression considering stochastic disturbance

Author

Listed:
  • Dayuan Wu

    (Chongqing University)

  • Ping Yan

    (Chongqing University)

  • You Guo

    (Chongqing University)

  • Han Zhou

    (Chongqing University)

  • Jian Chen

    (Chongqing University
    Chongqing Machine Tool (Group) Co., Ltd)

Abstract

Gear machining precision prediction is a challenging research topic because there are many influencing factors in the process of gear machining in terms of stochastic disturbance and hidden variables. To address this issue, a method that can predict gear manufacturing errors based on parameter significance estimations and probability regression is proposed in this paper. First, an adaptive machining quality evaluative function is designed to preprocess the raw precision detection data. Then, the key precision indices are extracted using a correlation and significance estimation (CSES) based on the modified density peak clustering (DPC) algorithm. A grading function is also designed, which can describe the precision grading of machined gear workpieces. Then, the significance estimation and attribution reduction of gear manufacturing parameters are performed using rough set theory. Finally, an adaptive variational inference Gaussian mixture regression (AVIGMR) model for gear machining error prediction is developed. The experimental results show that the proposed method has decent predictive capability with most gear precision detection indices and achieves superior comprehensive performance compared to eleven other regression algorithms.

Suggested Citation

  • Dayuan Wu & Ping Yan & You Guo & Han Zhou & Jian Chen, 2022. "A gear machining error prediction method based on adaptive Gaussian mixture regression considering stochastic disturbance," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2321-2339, December.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:8:d:10.1007_s10845-021-01791-2
    DOI: 10.1007/s10845-021-01791-2
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    References listed on IDEAS

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    1. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
    2. Liang Tian & Yu Luo, 2020. "A study on the prediction of inherent deformation in fillet-welded joint using support vector machine and genetic optimization algorithm," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 575-596, March.
    3. Zhenyu Liu & Donghao Zhang & Weiqiang Jia & Xianke Lin & Hui Liu, 2020. "An adversarial bidirectional serial–parallel LSTM-based QTD framework for product quality prediction," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1511-1529, August.
    4. Bodnar, Taras & Okhrin, Yarema, 2008. "Properties of the singular, inverse and generalized inverse partitioned Wishart distributions," Journal of Multivariate Analysis, Elsevier, vol. 99(10), pages 2389-2405, November.
    5. Roman Stryczek, 2016. "A metaheuristic for fast machining error compensation," Journal of Intelligent Manufacturing, Springer, vol. 27(6), pages 1209-1220, December.
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    Cited by:

    1. Yu-Yue Yu & Da-Ming Shi & Han Ding & Xiao-Ming Zhang, 2025. "Prediction of thin-walled workpiece machining error: a transfer learning approach," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2803-2827, April.
    2. Zehua Wang & Sibao Wang & Shilong Wang & Zengya Zhao & Zhifeng Tian, 2025. "An intelligent prediction method of surface residual stresses based on multi-source heterogeneous data," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 441-457, January.

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