Open AccessA Hybrid Chatter Detection Method Based on WPD, SSA, and SVM-PSO
Author(s) -
Erhua Wang,
Ping Yan,
Jie Liu
Publication year - 2020
Publication title -
shock and vibration
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 45
eISSN - 1875-9203
pISSN - 1070-9622
DOI - 10.1155/2020/7943807
Subject(s) - particle swarm optimization , vibration , machining , engineering , support vector machine , feature extraction , feature (linguistics) , wavelet packet decomposition , control theory (sociology) , cutting tool , wavelet , pattern recognition (psychology) , wavelet transform , artificial intelligence , computer science , algorithm , mechanical engineering , acoustics , linguistics , philosophy , physics , control (management)
As a kind of self-excited vibrations, chatter vibration is extremely common in end milling, especially in high-speed cutting processes. It affects the machining accuracy of products and decreases the processing efficiency of machine tools. Thus it is very crucial to develop an effective condition monitoring system to extract the chatter feature before chatter vibration grows. In this paper, a hybrid chatter detection method (HCDM) is proposed for chatter feature extraction and classification in end milling. Firstly, wavelet packet decomposition is employed to decompose cutting vibration signals into a series of wavelet coefficients, and the signals of each frequency band are reconstructed. Secondly, fast Fourier transform and singular spectrum analysis are chosen to obtain the chatter features. Furthermore, the support vector machine model is optimized by particle swarm optimization to recognize the cutting states in end milling. At last, cutting experiments of 300 M steel under different machining conditions are conducted, and the results indicate that the proposed HCDM can distinguish the stable, transition, and chatter states accurately and rapidly in end milling.