A real-time monitoring system for milling cutter breakage was researched and developed,which is based on the cutting power signals and uses the neural network technology for data processing.

Data acquisition and pretreatment of cutting power in milling process;

Introduced a kind of new method by means of frequency track method to measure cutting power of woodworking machines.

This paper introduce a kind of studying method for testing cutting power to freosaw blade on the woodworking push table saw machine.

The Realization of on-line monitoring machine cutting force signal based on DSP;

In order to erase the disturbance of random noise in cutting force signal,a denoising algorithm based on the convolution wavelet packet transformation is put forward and the detail step of algorithm is given.

And the cutting force signal, which is the singular signal when the tool is worn, is used to test the performance of these wavelet bases.

It is known that the cutting sound signal can reflect tool wear condition.

This paper researches on the tool wear condition monitoring by cutting sound signal and workpiece surface texture based on analysis of the relative situation.

Tool wear model is presented by firstly extracting power signal eigenvalue through tool wear analysis of special spiral rod milling,and then establishing the mapping between the signal eignevalue and the tool wear value.

The effect of tool wear and breakage on the feature of power signal in time and frequency domain is studied based on a large amount of experiments, and it is indicated that there is a good relationship between tool condition and power consumption or magnitude at the spindle rotation frequency.

The acoustic emission signal and the power signal of grinding wheel motor will change when wheel is dulled or broken in grinding process.