期刊
INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
卷 192, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijmecsci.2020.106111
关键词
Hybrid driven; Surrogate model; Bayesian framework; Machining errors; Thin-walled parts
资金
- National Natural Science Foundation of China [51625502]
- National Key Research and Development Program of China [2018YFB1701904]
The study introduces a hybrid driven approach integrating surrogate model and Bayesian framework to predict machining errors of thin-walled parts, resulting in higher efficiency and accuracy in comparison to mechanism or data-driven models.
Thin-walled structural parts are widely used in aerospace industry, the prediction of machining errors of these parts has attracted more and more attention over the past decades. Due to the complicated time-varying nonlinear characteristics and the lack of experimental data, it is difficult to use the mechanism model or data-driven model to predict the machining errors directly. To solve those problems, a hybrid driven approach to integrate surrogate model and Bayesian framework is proposed to predict the machining errors of thin-walled parts. The Gaussian process regression algorithm is embedded in the mechanism model to predict the cutting forces and flexibility, so as to establish the surrogate model for the prediction of machining errors. Besides, the surrogate model is calibrated by considering the uncertainties of cutting forces and tool wear. Five unknown calibration coefficients are analyzed through the Bayesian framework and determined by the Markov Chain Monte Carlo algorithm. A hybrid driven approach is introduced to train the prediction model. Both simulation data through mechanism analysis and experimental data through technological test are extracted. Compared with the mechanism model or data-driven model, the testing results have indicated that the proposed prediction model has higher efficiency and accuracy. The prediction time of a single point is reduced to 5.46ms and the root mean square error of the proposed prediction model is 4.5137 mu m.
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