Abstract
In order to research the relationship between grinding wheel wear and the signal of grinding strength and grinding vibration, the grinding strength signal and grinding vibration signal under different wear condition were carried on digital processing by time-domain, frequency-domain, and wavelet-pocket analysis, and characteristic signal reflecting grinding wheel wear condition was obtained. Grinding wheel wear was monitored by time-domain statistics average value of grinding strength and energy value of three layers wavelet-pocket decomposition frequency band. The method how to set design parameters of neural network is introduced, and their value in condition monitoring network is determined. Mapping model of grinding wheel wear and characteristic signal is established. Recognition effect is satisfied in the experiment of grinding wheel wear condition monitoring. It confirmed the model is reliable and effective. The result shows that the new intelligent monitoring method is effective on monitoring grinding wheel deactivation condition. One new method of diamond grinding wheel wear condition monitoring under precision and ultra-precision grinding is introduced.
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