The Experimental Research On Acoustic Emission Of Engineering Ceramic In High Efficiency Deep Grinding

In the recent several years, the AE(acoustic emission) technology obtains the universal attention in the aspect of grinding process. Its theory and method have become an important research area of the modern manufacture system for using them to monitor grinding processing condition. In order to enhance the efficiency of grinding processing and improve the quality of product, the research of AE signal is extremely essential for grinding processing, especially HEDG(High Efficiency Deep Grinding) processing.This paper first introduces the development and actuality of engineering ceramic, HEDG and AE technology, and induces the research condition of AE technology in grinding processing, and outlines the basic theory of AE technology. Basis of this, it forms the idea of experimental study of AE in the HEDG processing.According to the actual grinding processing process, this paper has developed the virtual instrument acquisition system of AE signal using the corresponding hardware and software and built the monitoring system of AE experiment. At the super high speed grinding machine, which is developed by Hunan University, this paper carries on the experimental study of AE about two kinds of engineering ceramic materials: PSZ and Al₂O₃, in HEDG. Through the results of experimental study, the paper analyzes the change rule of AE signal with different grinding wheel speed, table speed, depth of cut in the grinding of PSZ and Al₂O₃, and analyzes the influence of two kinds of materials on AE signal at the same grinding parameters too. The paper studies the change of AE signal, when the grinding wheel and the work piece contact with each other, and the change after the truing of the wheel. In the basis of analysis at time domain, the paper has carried on the spectrum analysis to the AE signal, and analyzes the distributed scope of frequency spectrum of AE signal in HEDG process. The results of experimental study indicate the AE signal has the good corresponding relations with the grinding process condition, and we can utilize the change rule of AE signal in grinding process to monitor the grinding process.The final of this paper predicts the roughness of work piece surface using three AE characteristic parameters in the method of BP neural network. This is a beneficial attempt of using AE technology to monitor the roughness of work piece surface.