Investigation on Chatter Monitoring of Composite Milling Edge Based on the Selection of Sensitive Frequency Band of Wavelet Packet

doi:10.3901/JME.2022.03.140

Abstract

Abstract: A novel energy entropy chatter monitoring method based on the selection of sensitive frequency band of wavelet packet is proposed to solve the problem of the initial chatter frequency submerged in the milling of large aviation skin composite materials. Firstly, the frequency elimination algorithm (FEA) is performed to remove the spindle rotation frequency, tooth passing frequency and their harmonics on the signals collected by the edge milling. Secondly, wavelet packet transform (WPD) is applied to decompose the filtered signal into a set of frequency bands and the frequency band distribution characteristics of WPD are analyzed. Then, sensitive frequency bands containing rich chatter information are selected as the research object based on the frequency band correlation coefficient ρ and energy ratio fluctuation variance σ² and energy entropy is extracted as the chatter monitoring feature. Meanwhile, PauTa criterion is used for determining the chatter monitoring threshold of the system. Finally, the simulation signal and experimental signal are employed to verify the effectiveness of the algorithm. The results showed that the energy entropy resolution ratio of the signal is increased by 202% after the FEA and sensitive frequency band selection, compared with the original signal, and the system chatter condition can be detected 0.5s earlier.

Key words: wavelet packet decomposition, sensitive frequency band, chatter, energy entropy, frequency elimination algorithm

CLC Number:

TH113

ZHANG Lei, ZHENG Kan, SUN Lianjun, SUN Hongwei, XUE Feng, WANG Tao. Investigation on Chatter Monitoring of Composite Milling Edge Based on the Selection of Sensitive Frequency Band of Wavelet Packet[J]. Journal of Mechanical Engineering, 2022, 58(3): 140-148.

References

[1] LI Z Q,LIU Q,YUAN S M,et al. Prediction of dynamic cutting force and regenerative chatter stability in inserted cutters milling[J]. Chinese Journal of Mechanical Engineering,2013,27(3):555-563.
[2] YANG K,WANG G F,DONG Y,et al. Early chatter identification based on an optimized variational mode decomposition[J]. Mechanical Systems and Signal Processing,2019,115:238-254.
[3] CHEN H G,SHEN J Y,CHEN W H,et al. Grinding chatter detection and identification based on BEMD and LSSVM[J]. Chinese Journal of Mechanical Engineering,2019,32(1):1.
[4] MELIH C Y,FIRAS A K,ANDREAS O,Ensemble empirical mode decomposition:A noise assisted data analysis method On transfer learning for chatter detection in turning using wavelet packet transform and ensemble empirical mode decomposition[J]. CIRP Journal of Manufacturing Science and Technology,2020,28:118-135.
[5] 王志学,刘献礼,李茂月,等. 切削加工颤振智能监控技术[J]. 机械工程学报,2020,56(24):1-23. WANG Zhixue,LIU Xianli,LI Maoyue,et al. Intelligent monitoring and control technology of cutting chatter[J]. Journal of Mechanical Engineering,2020,56(24):1-23.
[6] JI Y J,WANG X B,LIU Z B,et al. Early milling chatter identification by improved empirical mode decomposition and multi-indicator synthetic evaluation[J]. Journal of Sound and Vibration,2018,433:138-159.
[7] CAO H R,ZHOU K,CHEN X F. Chatter identification in end milling process based on EEMD and nonlinear dimensionless indicators[J]. International Journal of Machine Tools & Manufacture,2015,92:52-59.
[8] LI X H,WANG S K,HUANG X W,et al. Milling chatter detection based on VMD and difference of power spectral entropy[J]. The International Journal of Advanced Manufacturing Technology,2020,111:2051-2063.
[9] WANG Y,ZHANG M K,TANG X W,et al. A kMap optimized VMD‑SVM model for milling chatter detection with an industrial robot[J]. Journal of Intelligent Manufacturing,2021:01736-9.
[10] ZHANG Z,LIN H G,MENG G,et al. Chatter detection in milling process based on the energy entropy of VMD and WPD[J]. International Journal of Machine Tools & Manufacture,2016,108:106-112.
[11] 张智,刘成颖,刘辛军,等. 采用小波包能量熵的铣削振动状态分析方法研究[J]. 机械工程学报,2018,54(21):57-62. ZHANG Zhi,LIU Chengyin,LIU Xinjun,et al. Analysis of milling vibration state based on the energy entropy of WPD[J]. Journal of Mechanical Engineering,2018,54(21):57-62.
[12] 薛雷,曾宏伟,覃程锦,等. 采用同步压缩变换和能量熵的机器人加工颤振监测方法[J]. 西安交通大学学报,2019,53(8):24-30,89. XUE Lei,ZENG Hongwei,TAN Chengjin,et al. A chatter monitoring method for robotic machining using synchro-squeezed transform and energy entropy[J]. Journal of Xi'an Jiaotong University,2019,53(8):24-30,89.
[13] GENG D X,ZHANG D Y,XU Y G,et al. Rotary ultrasonic elliptical machining for side milling of CFRP:Tool performance and surface integrity[J]. Ultrasonics,2015,59:128-137.
[14] NORBERT G J,PAULO D,TIBOR S. Advanced cutting tools and technologies for drilling carbon fibre reinforced polymer (CFRP) composites:A review[J]. Composites Part A,2019,125:10552.
[15] 李洁平. 铣削加工颤振试验设计及其应用[D]. 武汉:华中科技大学,2019. LI Jieping. Design and application of milling chatter tests[D]. Wuhan:Huazhong University of Science and Technology,2019.
[16] 路敬祎,马雯萍,叶东,等. 基于VMD的声音信号增强算法研究[J]. 机械工程学报,2018,54(10):10-15. LU Jinyi,MA WenPin,YE Dong,et al. Algorithm of sound signal enhancement based on VMD[J]. Journal of Mechanical Engineering,2018,54(10):10-15.
[17] 陈仁祥,汤宝平,吕中亮. 基于Wavelet leader和优化的等距映射算法的回转支承自适应特征提取[J]. 振动.测试与诊断,2012,32(4):542-546,685. CHEN Renxiang,TANG Baoping,LÜ Zhongliang. Ensemble empirical mode decomposition de-noising method based on correlation coefficients for vibration signal of rotor system[J]. Journal of Vibration Measurement & Diagnosis,2012,32(4):542-546,685.
[18] MARCEL C,WOLFGANG H,YUSUF A. Chatter stability in robotic milling[J]. Robotics and Computer Integrated Manufacturing,2019,55:11-18.
[19] 钟怀兵,熊伟丽. 一种带奇异点检测和补偿的GPR在线软测量方法[J]. 南京理工大学学报,2017,41(4):503-510. ZHONG Huaibing,XIONG Weili. Online soft sensor method based on GPR with test and compensation for singular point[J]. Journal of Nanjing University of Science and Technology,2017,41(4):503-510.
[20] SUN L J,ZHENG K,LIAO W H,et al. Investigation on chatter stability of robotic rotary ultrasonic milling[J]. Robotics and Computer Integrated Manufacturing,2020,63:101911.
[21] TAO J F,ZENG H W,QIN C J,et al. Chatter detection in robotic drilling operations combining multi-synchrosqueezing transform and energy entropy[J]. The International Journal of Advanced Manufacturing Technology,2019,105:2879-2890.