Study On Wear Of Diamond Grinding Head In Rotary Ultrasonic Machining Based On Acoustic Emission

Zirconia ceramic material is a kind of non-metallic material with wide application prospect.It is difficult to process due to its high hardness and brittleness.Since rotary ultrasonic machining technology can reduce the cutting force and improve the machining efficiency in the machining process,it can replace the traditional machining method for the machining of zirconia ceramics.In the process of automatic production and intelligent manufacturing,the detection of grinding head wear is particularly critical,so it is of great significance to realize the online detection of grinding head wear in rotary ultrasonic machining.In this paper,a method based on acoustic emission detection technology is proposed to realize the online detection of the wear degree of the grinding head in the rotary ultrasonic machining process.The acoustic emission signal generated in the rotary ultrasonic machining process is detected by acoustic emission detection technology.The energy spectrum coefficients of each layer of the acoustic emission signal are extracted by wavelet analysis.The artificial neural network and support vector machine are used to realize the automatic identification of the wear degree of the grinding head.The main research contents are as follows:1.The advantages of rotary ultrasonic machining compared with traditional machining are explored through experiments.It is proved that rotary ultrasonic machining can reduce the cutting force and improve the surface quality of workpiece compared with traditional machining from two aspects of cutting force and workpiece surface morphology.The most suitable processing parameters for rotary ultrasonic machining of zirconia ceramics are explored.2.The wear experiment of diamond grinding head in rotary ultrasonic machining was carried out,and the whole process of wear was detected by acoustic emission testing technology.By analyzing the time domain and frequency domain signals of AE signal,the relationship between AE signal and grinding head wear is established.Through wavelet analysis,the energy spectrum coefficient of acoustic emission signal is extracted,which is used for automatic identification of grinding head wear.3.With the help of artificial neural network and support vector machine technology,the methods of automatically identifying the wear state of diamond grinding head are explored.The feasibility of the two methods is verified by the actual processing data,and the advantages and disadvantages of each method are compared.