Research on Monitoring Technology of End Milling Cutter Breaking in Machine Based on Optimized SVM Model
With the continuous improvement of machining accuracy requirements,there is an increasing demand for real-time monitoring of tool wear or blade breakage during cutting.In this paper,acoustic emission and spindle power are used as monitoring signals.By extracting effective features in time domain,frequency domain and time-frequency domain,a SVM monitoring model based on fusion signal is constructed.Mesh search,particle swarm opti-mization and genetic algorithm were used to optimize SVM model parameters,and the monitoring effect of flat end milling cutter was compared in actual cutting environment.The results show that the SVM model based on genetic algorithm optimization has the best effect on the state monitoring of milling cutter breakage.
acoustic emissionspindle power signalsblade breakage monitoringgenetic algorithmSVM model
万方数据
维普
