Abstract
According to the non-linear and non-stationary characteristics of the vibration signal in the roller grinding manufacturing process, the background signals, namely the vibration signal when the grinder is idling and the vibration signal during contact grinding, are collected separately, and the signal is decomposed into multiple by the method of CEEMDAN modal decomposition. Then several components are removed by correlation analysis. Finally, the signal is denoised and analyzed by wavelet threshold denoising, which lays the foundation for the digital and intelligent grinding of diamond rollers by curve grinders. The results show that most of the grinding noise is in the low frequency, which is mainly caused by the internal friction of the machine tool; the frequency of the grinding vibration signal is mainly distributed around the two frequencies of 250hz and 350hz.
Highlights
As a new generation of shaping and dressing tools, diamond rollers have the characteristics of complex contours and high machining accuracy
In the analysis of electrical imaging logging data, Xu Fanghui [7] and others performed wavelet threshold denoising on the high frequency IMF components decomposed by EMD, and found that the noise and interference in the output static image were significantly reduced
The CEEMDAN modal decomposition method is used to smooth the collected vibration signals, and after removing several components through correlation analysis, the remaining characteristic modal functions are denoised by threshold filtering
Summary
As a new generation of shaping and dressing tools, diamond rollers have the characteristics of complex contours and high machining accuracy. It is mainly used in gears, bearings, precision ball screws and other fields. Guo Li of Hunan University [4][5] and others used wavelet analysis and GA-SVM to analyze the acoustic emission signal so that the accuracy of the judgment of the wear state of the diamond grinding wheel reached 100%. Based on the characteristics of nonlinear and non-stationary vibration signals in the diamond roller grinding process, this paper combines the modal decomposition method with fixed threshold filtering. The CEEMDAN modal decomposition method is used to smooth the collected vibration signals, and after removing several components through correlation analysis, the remaining characteristic modal functions are denoised by threshold filtering
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