基于主轴电流信号多特征融合的刀具磨损状态监测

为实现在正常生产条件下进行刀具磨损的长期在线监测,提出了基于主轴电流信号和粒子群优化支持向量机模型(PSO-SVM)的刀具磨损状态间接监测方法.首先对数控机床主轴电机电流信号进行分析,将与刀具磨损相关的主轴电流信号多个特征参数和EMD能量熵进行特征融合作为输入特征向量;其次,通过粒子群寻优算法(PSO)对支持向量机模型...

Monitoring cutting tool wear based on spindle current signal multi-feature fusion

In order to gain long-term online monitoring cutting tool wear data under normal cutting conditions,a new method for monitoring cutting tool wear was proposed based on the spindle current signal and particle swarm optimization support vector machine(PSO-SVM)model.Firstly,the spindle motor current signals of CNC machine tool were analyzed.Multiple feature parameters related to tool wear and the EMD energy entropy were fused as an input feature vector.Secondly,the SVM model parameters were optimized by the PSO algorithm.The tool wear condition recognition model was established based on the spindle current signal and PSO-SVM theory.Finally,the spindle current signals of the vertical machining center under different tool wear conditions were collected by the experiment.The proposed method was compared with traditional SVM model and BP neural network model.The results show that the proposed method has higher recognition accuracy and better generalization ability.The proposed method can realize the long-term online monitoring of the tool wear condition.