Real-time and accurate monitoring of the wear state of milling cutter is of great significance to improve the machining quality and efficiency.In this paper,a milling cutter wear state recognition method based on digital twin is proposed.This method combines VMD-MPE feature extraction method and GA-SVM state recognition model to construct digital twins to monitor the wear state of milling cutter in real time.Firstly,the variational mode decomposition(VMD)algorithm is used to decompose the vibration sig-nal of the milling cutter to obtain the modal component containing the wear state information.Secondly,the multi-scale permutation entropy(MPE)is introduced to extract the nonlinear dynamic characteristics of the milling cutter from the modal components containing the wear state information,and the average value of the multi-scale permutation entropy of each effective modal component is taken as the characteristic matrix.Finally,the genetic algorithm(GA)is used to optimize the support vector machine(SVM)to construct the milling cutter wear state recognition model.The experimental results show that the digital twin construc-ted in this paper has a good recognition effect,and its recognition accuracy can reach 97.33% .
关键词
数字孪生/刀具磨损/状态识别/变分模态分解/多尺度排列熵/支持向量机
Key words
digital twin/tool wear/state recognition/variational mode decomposition/multi-scale permuta-tion entropy/support vector machine
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