Open AccessChatter Identification in End Milling Process Based on Cutting Force Signal Processing
Author(s) -
Minh-Quang Tran,
M K Liu
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/654/1/012001
Subject(s) - short time fourier transform , spectrogram , signal (programming language) , process (computing) , tool wear , continuous wavelet transform , fourier transform , signal processing , vibration , time–frequency analysis , frequency domain , end milling , engineering , wavelet transform , computer science , wavelet , acoustics , mechanical engineering , artificial intelligence , discrete wavelet transform , mathematics , machining , fourier analysis , computer vision , electronic engineering , digital signal processing , mathematical analysis , filter (signal processing) , operating system , programming language , physics
During milling process, chatter is one of the major limitations of productivity, it has more negative effects on the surface finish, dimensional accuracy and tool life. It is thus necessary to detect and avoid chatter occurrence. In this study, a chatter detection approach based on cutting force signal is presented. The milling force signals were measured by cutting force sensor and then analyzed by using time-frequency analysis approaches such as short-time Fourier transform (STFT) and continuous wavelet transform (CWT) in order to identify chatter. The different sets of cutting conditions were utilized to recognize chatter presence. The spectrogram and scalogram results of the force signals represented a great difference in frequency content between stable and unstable cuttings. The analysis results were then validated by cutting force behavior in the time domain.