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- Title
基于SFS-SVR的高速铣削刀具剩余使用寿命预测.
- Authors
黄贤振; 孙良仕; 高娓; 李禹雄
- Abstract
In high speed milling operations, it is urgent to accurately predict the remaining useful life of cutting tools to determine the best time to replace them, but there is often a problem of insufficient historical data in the prediction. Therefore, a method for remaining useful life prediction of cutting tools in small sample space is proposed, which is based on the support vector regression (SVR) method. And the stochastic fractal search (SFS) algorithm is used to optimize the key parameters of SVR. Compared with the traditional method, it obtains better model parameters and faster convergence speed. Finally, the as-adopted method is compared with the hidden Markov model (HMM) approach. The accuracy rate increases from 0.6277 to 0.8199, which provides a reliable reference for tool replacement.
- Subjects
REMAINING useful life; HIDDEN Markov models; SPEED; CUTTING tools; MILLING cutters
- Publication
Journal of Northeastern University (Natural Science), 2023, Vol 44, Issue 6, p824
- ISSN
1005-3026
- Publication type
Article
- DOI
10.12068/j.issn.1005-3026.2023.06.009