Research on acoustic emission intelligent monitoring in grinding engineering ceramics
Acoustic emission (AE) signal analysis by use of short time Fourier transform is used to monitor the grinding heat by ues of laser. The relationship between the acoustic emission signal of high speed grinding of engineering ceramics and grinding force, grinding temperature are studied. High precision AE monitoring of diamond grinding wheel wear in engineering ceramics grinding were carried out. The variance of wavelet decomposition coefficient of AE signal in alumina grinding is used as the input feature of support vector machine. The empirical mode decomposition (EMD) of grinding AE signal is used to extract the effective value, variance and energy coefficient of its intrinsic mode function as the input features of least squares support vector machine. The optimized BP neural network is used to monitor the grinding surface roughness with high precision by use of AE. The research solved the problem of acoustic emission monitoring in engineering ceramics grinding and laid the foundation for its practical application!