Study On Monitoring Method For Grinding Wheel Dull State Based On Acoustic Emission And Wavelet Analysis

Usually, the grinding machining is the final procedure in the process of the exact parts manufacture. The grinding wheel dull is ineluctable in the manufacture procedure, which influences the efficiency and the quality of products. Therefore, the dull state of the wheel should be monitoring effectively in order to make sure the dressing period of the wheel, which plays a very important role in debasing the machining cost, enhancing the work efficiency, promoting the machining quality.Through AE signals analyzing, it can be found that the signal produced in engineering ceramic grinding varying with the grinding wheel dull state, so the AE signal is chose as the monitoring signal to monitor the grinding wheel dull state. The application of wavelet analysis on acoustic emission signal processing was studied in the thesis after analyzing the parameter analytical method and the frequency spectrum. The method based on AE technique and wavelet analysis was presented to monitor the wheel grinding dull state.Firstly, the influence of the noises is discussed, and the frequencies of the noises are differentiated. The interference of the noises is decreased maximally by choosing the appropriate filter. The rules of how to select the suitable wavelets for acoustic emission signal processing were proposed based on the features of acoustic emission signal and the theory of the wavelet analysis, In terms of the rules, Daubechies wavelet, Symlets wavelet and Coiflets wavelet are regarded to be suitable for acoustic emission. And the maximum decomposition level of wavelet analysis was also confirmed. The research result above is important to use wavelet analysis for acoustic emission signal processing. And then the signals are decomposed to several layers.Secondly, the concept of the wavelet energy coefficient is defined. The feature extraction method for acoustic emission signal based on wavelet energy coefficient is proposed. The wavelet energy coefficients of each dull condition are extracted. Experimental results showed that the wavelet energy coefficients are accordant to the dull state, and different among states. So the wavelet energy coefficients can be used as the input parameters of the dull state discriminator.The final of this paper, the improved arithmetic of the BP neural network is chose, and the three-layer Back-Propagation artificial neural network is built up to recognize the grinding wheel dull state. Meanwhile, the chose correlative parameters are used to determine the optimized neural network structure. The sample signals are used to training and simulating. The simulation results indicate that the trained network can be used to recognize the wheel dull states effectively (the ratio of the recognition can achieve to 90% around), and achieve to anticipated goal. The research results of the thesis have important significance and practical value to promote the development of technology of the monitoring grinding wheel dull state and to improve the quality and efficiency of the grinding process.