An Optimized VMD Method for Predicting Milling Cutter Wear Using Vibration Signal

被引:10
|
作者
Chang, Hao [1 ,2 ]
Gao, Feng [1 ,2 ]
Li, Yan [1 ,2 ]
Wei, Xiaoqing [3 ]
Gao, Chuang [4 ]
Chang, Lihong [5 ]
机构
[1] Xian Univ Technol, Key Lab NC Machine Tools & Integrated Mfg Equipme, Minist Educ, Xian 710048, Peoples R China
[2] Xian Univ Technol, Key Lab Mfg Equipment Shaanxi Prov, Xian 710048, Peoples R China
[3] Shandong Deed Precis Machine Tool Co Ltd, Jining 272114, Peoples R China
[4] Xian Jingdiao Precis Mech Engn Co Ltd, Xian 710119, Peoples R China
[5] Beijing Univ Agr, Coll Humanities & Urban Rural Dev, Dept Rural Reg Dev, Beijing 102206, Peoples R China
基金
中国国家自然科学基金;
关键词
identification of the tool wear; variational mode decomposition; differential evolution; envelope entropy; cross-correlation coefficient; EMPIRICAL MODE DECOMPOSITION; TOOL;
D O I
10.3390/machines10070548
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Tool wear has a negative impact on machining quality and efficiency. As for the nonlinear and non-stationary characteristics of vibration signals and strong background noises during the milling process, an identification method of the milling cutter wear state based on the optimized Variational Mode Decomposition (VMD) was proposed, in which the objective function is to minimize the Envelope Entropy (Ep); the various modes of the vibration signal are decomposed using the self-adaptive optimization parameters with Differential Evolution (DE). According to the cross-correlation coefficient in the frequency domain between Intrinsic Mode Function (IMF) and the original signals, the informative IMF components were selected as the sensitive IMF components to superimpose the reconstruction signal and extract the eigenvalues. The mapping relationship between the eigenvalues and the milling cutter wear degree is established by the Naive Bayes classifier method. The experimental results under the various operation conditions indicate that the proposed optimized VMD method possesses an excellent generalization performance. Compared with Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD), it has better denoising capacity, and so can improve the identification accuracy of the milling cutter wear. Therefore, the processing quality and production efficiency are ensured effectively.
引用
收藏
页数:18
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