BP-Neural-Network-Based Tool Wear Monitoring by Using Wav elet Decomposition of the Power Spectrum

In a drilling process, the power spectr um of the drilling force is related to the tool wear and is widely applied in the monitoring of tool wear. But the feature extraction and identification of the po wer spectrum have always been an unresolved difficult problem. This paper solves it through decomposition of the power spectrum in multilayers using wavelet tra nsform and extraction of the low frequency decomposition coefficient as the enve lope information of the power spectrum. Intelligent identification of the tool w ear status is achieved in the drilling process through fusing the wavelet decomp osition coefficient of the power spectrum by using a BP(Back Propagation) neural network. The experimental results show that the features of the power spectrum can be extracted efficiently through this method, and the trained neural network s show high identification precision and the ability of extension.