Research On Drill Bit Condition Monitoring Technology Of Vibration Drilling

The condition of the cutting tool directly affects the machining quality of the workpiece during the machining process,so that establishing a real-time and highly reliable drill bit condition monitoring system is of great significance to prolong the service life of drill bit as well as enhance the production efficiency.First,a drill wear condition monitoring system of vibration drilling was established,and acoustic emission signal and drilling force signal in different wear condition were collected synchronously by using acoustic emission sensor and piezoelectric sensor in this paper.Due to the influence of processing conditions,the signals collected by the sensors are often mixed with plenty of noise during the actual vibration drilling process.Therefore,the wavelet threshold de-noising method was used to de-noise the acoustic emission signal and the drilling force signal in pretreatment,which improved the signal-to-noise ratio of the signals.Then,the acoustic emission signal and the drilling force signal were analyzed and processed after de-noising.Mean,variance and root mean square of the signal were calculated in time domain,power spectrum analysis was carried out in frequency domain,wavelet decomposition was carried out in time-frequency domain,and wavelet energy coefficients of each frequency band were extracted.According to the analysis results of the eigenvalues of signals in different wear condition,eight eigenvalues with high correlation with drill condition were chosen as the input eigenvector of BP neural network which include the RMS value of the acoustic emission signal,the wavelet energy coefficient of D1,D2 and D3 frequency band of the acoustic emission signal,the mean and variance of drilling force signal,the wavelet energy coefficient of D4,A5 frequency band of drilling force signal.Finally,a drilling bit condition recognition model of vibration drilling was established based on BP neural network.And the corresponding relationship between the eigenvalues of monitoring signals and the condition of drill bit wear was established through the training of the sample data.Experiments on test samples prove that the model has a high recognition ability on the condition of vibration drilling bit.The drill wear condition monitoring system of vibration drilling established in this paper has practical significance for prolonging the service life of the drill bit and enhancing the production efficiency.