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International Standard Serial Number:
ISSN 1001-4551
Sponsor:
Zhejiang University;
Zhejiang Machinery and Electrical Group
Edited by:
Editorial of Journal of Mechanical & Electrical Engineering
Chief Editor:
ZHAO Qun
Vice Chief Editor:
TANG ren-zhong,
LUO Xiang-yang
Tel:
86-571-87041360,87239525
Fax:
86-571-87239571
Add:
No.9 Gaoguannong,Daxue Road,Hangzhou,China
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310009
E-mail:
meem_contribute@163.com
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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- Chinese Core Periodicals
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- Chinese Sci-tech Core Periodicals
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- SA, INSPEC Indexed
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- CSA: T Indexed
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- UPD:Indexed
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2010 Zhejiang Information Institute of Mechinery Industry
Technical Support：Hangzhou Bory science and technology
