Analysis and selection of eigenvalues of vibration signals in cutting tool milling

This paper designs an experimental scheme to monitor the tool wear state by extracting the vibration signal of tool wear. The data acquisition and signal processing hardware includes a T-type cutting tool, a vibration sensor, an amplifier, a data acquisition card, and a computer. In the time domain, the vibration signal is analyzed by statistical analysis method, and it is concluded that the variance of vibration signal of X-axis wear is positively correlated with the degree of tool wear. Moreover, the vibration signal is converted from time domain to frequency domain by Fourier transform, and the characteristic frequency bands of vibration signal are 2–4 kHz and 7–9 kHz in frequency domain. In addition, in time-frequency domain, the vibration signal is decomposed by wavelet packet decomposition technology, and the energy statistics of the decomposed frequency band is carried out. It is further judged that the energy percentage of 2.5–3.75 kHz and 7.5–8.25 kHz is closely related to tool wear, so the energy percentage of the two characteristic frequency bands is selected as the characteristic value of tool wear monitoring.