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.
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Acknowledgements
This research is supported by the“Chongqing Technology Innovation and Application Development Special Project (cstc2019jscx-mbdxX0016)”, and“Basic Scientific Research Business Expenses of Central Universities of Chongqing University (2019CGCG0004, 2019CGCG0003, 2019CDCGJX315)”.
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Wu, D., Yan, P., Guo, Y. et al. A gear machining error prediction method based on adaptive Gaussian mixture regression considering stochastic disturbance. J Intell Manuf 33, 2321–2339 (2022). https://doi.org/10.1007/s10845-021-01791-2
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DOI: https://doi.org/10.1007/s10845-021-01791-2