基于小波包敏感频带选择的复材铣边颤振监测研究

doi:10.3901/JME.2022.03.140

机械工程学报 ›› 2022, Vol. 58 ›› Issue (3): 140-148.doi: 10.3901/JME.2022.03.140

扫码分享

基于小波包敏感频带选择的复材铣边颤振监测研究

张磊, 郑侃, 孙连军, 孙红伟, 薛枫, 王涛

南京理工大学机械工程学院南京 210094

收稿日期:2021-02-19 修回日期:2021-06-23 出版日期:2022-02-05 发布日期:2022-03-19
通讯作者: 郑侃(通信作者),男,1983年出生,教授。主要研究方向为先进制造技术与装备。E-mail:zhengkan@njust.edu.cn
作者简介:张磊,男,1997年出生。主要研究方向为加工颤振在线监测。E-mail:zhanglei4938@outlook.com
基金资助:
国家自然科学基金资助项目（51861145405，52075265，91860132）。

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

ZHANG Lei, ZHENG Kan, SUN Lianjun, SUN Hongwei, XUE Feng, WANG Tao

School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094

Received:2021-02-19 Revised:2021-06-23 Online:2022-02-05 Published:2022-03-19

摘要/Abstract

摘要： 针对航空蒙皮类复合材料零件铣边颤振初期频率淹没问题，提出了一种基于小波包敏感频带选择的能量熵颤振监测方法。首先使用频率消除算法（Frequency elimination algorithm，FEA）对铣边采集信号进行去主轴转频、刀齿齿频及其倍频处理。其次对去频后的信号进行小波包分解（Wavelet packet decomposition，WPD），分析WPD的频带分布特性。然后基于频带相关系数ρ和能量比波动方差σ²选取颤振敏感频带作为颤振研究对象，提取分解后信号能量熵作为颤振监测量并基于拉依达准则确定系统颤振监测阈值。最后通过仿真信号和试验验证了所提算法的有效性。结果表明，经FEA和敏感频带筛选后信号的能量熵值分辨率提高了205%，且相较于未去频信号系统颤振监测响应时间能提前0.5 s。

关键词: 小波包分解, 敏感频带, 颤振, 能量熵, 频率消除算法

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

中图分类号:

TH113

张磊, 郑侃, 孙连军, 孙红伟, 薛枫, 王涛. 基于小波包敏感频带选择的复材铣边颤振监测研究[J]. 机械工程学报, 2022, 58(3): 140-148.

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.

参考文献

[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.

基于小波包敏感频带选择的复材铣边颤振监测研究

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

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	刘阔, 姜业明, 黄任杰, 李明禹, 陈虎, 王永青. 虑及颤振在线抑制的机床高效自适应加工技术研究[J]. 机械工程学报, 2024, 60(6): 104-113.
[2]	唐新姿, 李可翔, 何文双, 陈睿, 彭锐涛. 多工况风电叶片颤振相关性分析与气弹优化[J]. 机械工程学报, 2024, 60(20): 300-314.
[3]	李超, 张俊, 田辉, 赵万华. 综合振动信号能量比和幅值标准差的铣削颤振实时监测方法[J]. 机械工程学报, 2024, 60(14): 11-23.
[4]	谭志朴, 秦国华, 娄维达, 吴竹溪. 基于伯努利分布与混合驱动模型的铣削稳定性高精判断方法[J]. 机械工程学报, 2024, 60(11): 318-331.
[5]	孙朝阳, 彭芳瑜, 唐小卫, 闫蓉, 辛世豪, 吴嘉伟. 基于自适应变分模态分解与功率谱熵差的机器人铣削加工颤振类型辨识[J]. 机械工程学报, 2023, 59(9): 90-100.
[6]	王晓勇, 郜志英, 辛岩莉. 基于定量递归分析的冷轧颤振状态识别与预报[J]. 机械工程学报, 2023, 59(9): 137-145.
[7]	朱子俊, 朱祥龙, 董志刚, 康仁科, 鲍岩. 磨削加工颤振稳定性研究综述[J]. 机械工程学报, 2023, 59(21): 15-33.
[8]	娄维达, 秦国华, 王华敏, 吴竹溪. 基于复化Cotes积分和神经网络的稳定铣削工艺参数多目标优化方法[J]. 机械工程学报, 2023, 59(17): 279-290.
[9]	刘春景, 唐敦兵, 陈兴强, 魏天路. 基于改进完全离散化法铣削系统稳定性研究[J]. 机械工程学报, 2023, 59(15): 162-173.
[10]	叶松涛, 严思杰, 李文韬, 徐小虎, 陆家麟. 面向机器人铣削加工的刀尖动态特性分析与稳定性预测[J]. 机械工程学报, 2022, 58(17): 261-275.
[11]	王志学, 刘献礼, 李茂月, 王力翚, Steven Y. LIANG, 于福航. 考虑力致变形影响的薄壁件铣削多点接触稳定性预测[J]. 机械工程学报, 2022, 58(17): 309-320.
[12]	陈云, 侯亮, 刘文志, 卜祥建. 基于时域仿真法的断续铣削颤振预测[J]. 机械工程学报, 2021, 57(3): 98-106.
[13]	刘涛, 邓朝晖, 罗程耀, 吕黎曙, 李重阳, 万林林. 基于动态磨削深度的非圆轮廓高速磨削稳定性建模与分析[J]. 机械工程学报, 2021, 57(15): 264-274.
[14]	曹宏瑞, 李登辉, 刘金鑫, 张兴武, 陈雪峰. 智能主轴高速铣削颤振的模糊控制方法研究[J]. 机械工程学报, 2021, 57(13): 55-62.
[15]	赖勇能, 李庆峰, 仇健海, 杨建刚. 流固耦合作用下可倾轴承瓦块颤振失稳研究[J]. 机械工程学报, 2020, 56(9): 118-124.