Condition monitoring of deep-hole drilling process based on improved empirical wavelet de-noising and high multiple frequency components of rotation frequency

Abstract

The machining condition monitoring is an important task to improve product quality and avoid economic losses in deep-hole drilling. Out-of-roundness-tolerance is one of major defects in deep-hole drilling process. In this work, the spindle vibration signals generated in deep-hole drilling process are studied to seek the characteristics which can reveal the roundness error change. Considering the influence of background noise on monitoring effect and the sensitivity of high multiple frequency components of spindle vibration signal towards the roundness error change, an improved empirical wavelet de-noising method is proposed to remove the noise of the spindle vibration signal. The high multiple frequency components of the rotation frequency are extracted from the de-noised spindle vibration signal. Firstly, the spindle vibration signal is decomposed by empirical wavelet transform into a set of wavelet coefficients, and then an improved thresholding function is proposed to process the Fourier spectrum of wavelet coefficients for removing the noise from the wavelet coefficients. Secondly, the wavelet coefficients are reconstructed based on the de-noised wavelet coefficients, and the wavelet coefficients which contain high multiple components of rotation frequency are selected. Finally, the energy entropy of the selected wavelet coefficients is calculated as a roundness error monitoring indicator to detect the roundness error of deep hole. The proposed method is verified with well-designed deep-hole drilling tests. The test results show that the monitoring indicator can effectively reflect the roundness error change in deep-hole drilling process.