Journal of Northeastern University(Natural Science) ›› 2023, Vol. 44 ›› Issue (6): 824-831.DOI: 10.12068/j.issn.1005-3026.2023.06.009

• Mechanical Engineering • Previous Articles     Next Articles

Remaining Useful Life Prediction of Cutting Tools Based on SFS-SVR in High Speed Milling Operations

HUANG Xian-zhen1, SUN Liang-shi1, GAO Wei2, LI Yu-xiong1   

  1. 1. School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China; 2. China National Heavy Duty Truck Group Jinan Truck Co., Ltd., Jinan 250116, China.
  • Published:2023-06-20
  • Contact: HUANG Xian-zhen
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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.

Key words: high-speed milling; tool wear; remaining useful life; stochastic fractal search; support vector regression

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