Review on on⁃line monitoring of chatter in cutting process

被引:0
|
作者
Li Y. [1 ,3 ]
Dend Z. [2 ,3 ]
Liu T. [1 ,3 ]
Zhuo R. [1 ,3 ]
Li Z. [1 ,3 ]
Lv L. [1 ,3 ]
机构
[1] School of Mechanical Engineering, Hunan University of Science and Technology, Xiangtan
[2] Institute of Manufacturing Engineering, Huaqiao University, Xiamen
[3] Hunan Provincial Key Laboratory of High Efficiency and Precision Machining of Difficult-to-Cut Materials, Hunan University of Science and Technology, Xiangtan
基金
中国国家自然科学基金;
关键词
chatter recognition; cutting chatter; data acquisition; on-line monitoring; signal processing; supervised learning; unsuper⁃ vised learning;
D O I
10.7527/S1000-6893.2022.27562
中图分类号
学科分类号
摘要
Chatter is a widespread problem in aerospace manufacturing and other fields. In-depth research on on-line monitoring of chatter in cutting process is of great significance to further improve the suppression effect of chatter and ensure the stable operation of machining system. According to the real-time and accuracy requirements of the chatter online monitoring,this paper focuses on data acquisition,online feature extraction,and chatter recognition. Firstly,the characteristics of three kinds of chatter data acquisition methods are summarized,and then the application of chat⁃ ter features and the key factors affecting chatter feature extraction are elaborated and analyzed. Later,the characteris⁃ tics of chatter recognition techniques based on the supervised and unsupervised learning are compared and summa⁃ rized. Finally,problems existed in the current on-line chatter monitoring and the development trend in the future are discussed,which can provide reference for the research of on-line chatter monitoring in the future. © 2023 AAAS Press of Chinese Society of Aeronautics and Astronautics. All rights reserved.
引用
收藏
相关论文
共 122 条
  • [71] YAO Y C, CHEN Y H, Et al., Real-time chat⁃ ter detection and automatic suppression for intelligent spindles based on wavelet packet energy entropy and lo⁃ cal outlier factor algorithm[J], The International Journal of Advanced Manufacturing Technology, 103, 1-4, pp. 297-309, (2019)
  • [72] LI X, MEI D Q, CHEN Z C., Feature extraction of chat⁃ ter for precision hole boring processing based on EMD and HHT[J], Optics and Precision Engineering, 19, 6, pp. 1291-1297, (2011)
  • [73] JIA G F, SUN S Z,, WU Z., Application of empirical mode decomposition in analyzing cutting vibration signal [J], Hebei Journal of Industrial Science and Technology, 35, 3, pp. 215-219, (2018)
  • [74] SINGH B., Tool chatter prediction based on empirical mode decomposition and response sur⁃ face methodology[J], Measurement, 173, (2020)
  • [75] NI C B., The chatter identification in end milling based on combining EMD and WPD[J], The International Journal of Advanced Manufacturing Technology, 91, 9-12, pp. 3339-3348, (2017)
  • [76] QI T, WU Y F., Application of EEMD to sup⁃ pression of mode mixing in oscillation signals[J], Noise and Vibration Control, 30, 2, pp. 103-106, (2010)
  • [77] ZHOU H,, Et al., Timely online chat⁃ ter detection in end milling process[J], Mechanical Sys⁃ tems and Signal Processing, 75, pp. 668-688, (2016)
  • [78] SINGH B., A comparative study of EMD and EEMD approaches for identifying chatter fre⁃ quency in CNC turning[J], European Journal of Mechan⁃ ics - A/Solids, 73, pp. 381-393, (2018)
  • [79] SINGH B., Online monitoring of tool chatter in turning based on ensemble empirical mode decomposition and Teager Filter[J], Transactions of the Institute of Measurement and Control, 42, 6, pp. 1166-1179, (2020)
  • [80] HUANG N E., Ensemble empirical mode de⁃ composition:a noise-assisted data analysis method[J], Advances in Adaptive Data Analysis, 1, 1, pp. 1-41, (2009)