Experimental Scheme to Monitor Eigenvalues of Vibration Signals in Cutting Tool Milling | Chapter 6 | Current Topics and Emerging Issues in Materials Sciences Vol. 2

 In order to better resolve the problem of veracity of tool wear status forecasting, the extraction of feature principles of tool wear facts from the sensors is the basis for resolving the problem. This chapter designs an exploratory scheme to monitor the form wear state by extracting the quivering signal of tool wear. Milling tool wear state acknowledgment plays an important function in controlling the character of milled parts and lowering machine tool downtime. However, the traits of milling process limit the veracity and stability of form condition monitoring employing quivering signals. A T-type cutting finish, a vibration sensor, an speaker, a data acquisition badge, and a computer compensate the data addition and signal processing fittings. The vibration signal is statistically analysed in the time rule, and it is determined that the difference of the vibration signal made by X-axis wear is positively belonging to the level of tool wear. Moreover, the quivering signal is converted from period domain to commonness domain by Fourier transform, and the characteristic commonness bands of vibration signal are 2~4 kHz and 7~9 kHz deficiency domain.  The DB4 wavelet of Daubechies succession wavelets is used as the wavelet packet base, and the DB4 wavelet bundle base has features to a degree smoothness in addition to matching wavelet fast algorithms Wavelet small decomposition technology is used to extract the eigenvalues of shaking signals. In addition, the characteristics at an unspecified future time domain, repetitiveness domain and frequency rule are also conferred.  It is further judged that the strength percentage of 2.5~3.75 kHz and 7.5~8.25 kHz is carefully related to tool wear, so the strength percentage of two together characteristic frequency bands is picked as the characteristic value of tool wear listening.

Author(s) Details:

Liqiang Wang,

Electronic Engineering School, Tianjin University of Technology and Education, Tianjin, China.

Xiao Li,

Electronic Engineering School, Tianjin University of Technology and Education, Tianjin, China.

Bo Shi,

Electronic Engineering School, Tianjin University of Technology and Education, Tianjin, China.

Munyaradzi Munochiveyi,

Electrical and Electronics Engineering Department, University of Zimbabwe, Harare, Zimbabwe.

Please see the link here: https://stm.bookpi.org/CTEIMS-V2/article/view/11313