Abstract
The machine tool is the main execution unit in the cyber-physical system (CPS system), which can improve the product quality by dynamic monitoring and real-time perception of its wear status. In order to realise the online signal acquisition and monitoring of tool wear status, the spindle power signal acquisition system was implemented. The cutting force signal is used as contrast analysis. The HHT method and wavelet transform method are introduced to construct the tool wear coefficients, which are corresponding to the tool wear status. Compared with the wavelet transform, it is proved that Hilbert-Huang transform can restrain the noise signal effectively and improve the accuracy of the monitoring. Finally, the new tool wear monitoring method is applied to drilling 45# steel and titanium alloy TC4 to catch the tool wear state, and the power signal is used to carry out comprehensive online tool state monitoring. It is accurate and practical in the drilling test, which shows prospective usage in the near future.
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