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Milling cutter wear monitoring based on VMD energy weighting method and BWO-SVM
Published:2023-02-15 author:ZHAO Xiao-hui, YANG Wen-bin, HU Sheng, et al. Browse: 491 Check PDF documents
Milling cutter wear monitoring based on VMD energy 
weighting method and BWO-SVM


ZHAO Xiao-hui, YANG Wen-bin, HU Sheng, TAN Qi, PAN Yang

(School of Mechanical and Electrical Engineering, Xi'an Polytechnic University, Xi'an 710048, China)


Abstract: Aiming at the problem of large signal noise and low monitoring efficiency in milling cutter wear state monitoring, a method of milling cutter wear state monitoring based on the energy weighting method of variational modal decomposition (VMD) and black widow optimization (BWO)support vector machine (SVM) was proposed. Firstly, VMD was used to decompose the vibration signal generated during milling into a number of inherent modal function (IMF), and the IMF components containing wear state features were adapted to extract the signal reconstruction by energy-weighted synthetic cliff metrics, and features were extracted from the reconstructed signal. Then, the parameters of the SVM were optimized using the BWO algorithm to construct a BWO-SVM milling tool wear state monitoring model. Finally, experiments were carried out with the vibration data of the PHM Society 2010 milling cutter throughout its life cycle and verified by engineering cases. The results show that the proposed method is effective in noise reduction after adaptively extracting the effective components for signal reconstruction, and the training time of the optimized SVM by BWO is shortened to 25.142s compared with the SVM by genetic algorithm(GA) and particle swarm optimization algorithm(PSO), and the monitoring accuracy reaches 97.246%. The wear condition monitoring of milling cutters by this method can obtain faster recognition speed and higher accuracy, and improves the efficiency of milling cutter wear monitoring.

Key words:  mechanical friction and wear; variational modal decomposition(VMD); black widow optimization-support vector machine(BWO-SVM); intrinsic mode function(IMF) components; energy-weighted composite kurtosis; wear state monitoring model 
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