Abstract: Aiming at the problem of low accuracy and low reliability of acoustic emission prediction of grinding surface roughness, 200 sets of experimental data of acoustic emission prediction of surface roughness of nodular cast iron QT700-2 were obtained in surface grinding experiments. 13 characteristic parameters of grinding acoustic emission signals, such as the correlation number of four Intrinsic Mode Functions including empirical mode decomposition of grinding acoustic emission signal and waveform amplitude, root mean square value, variance, peak frequency and spectrum peak value of grinding acoustic emission signal,peak value of power spectrum, kurtosis, skewness and acoustic emission information entropy of grinding acoustic emission signal were obtained. Two prediction models, genetic algorithm support vector regression machine GA-SVR and particle swarm optimization support vector regression machine PSOSVR, were established. The 13 acoustic emission signal characteristic parameters extracted from the 200 sets of acoustic emission experimental data of grinding surface roughness were input into the two prediction models, GA-SVR and PSO-SVR, for repeated training and prediction to improve their reliability. The results show that GA-SVR and PSO-SVR have higher prediction accuracy. It lays a foundation for on-line intelligent monitoring of grinding surface roughness of nodular cast iron QT7002 crankshaft in automobile engine by grinding acoustic emission.

Key words:  grinding surface roughness; acoustic emission(AE); intrinsic mode functions; support vector regression machine; genetic algorithm(GA); particle swarm optimization(PSO)

LONG Hua, ZHU Qi, GUO Li, et al. Acoustic emission intelligent prediction of surface roughness in surface grinding of nodular cast iron［J］.Journal of Mechanical & Electrical Engineering, 2021,38(8):1076-1080.