基于占能比的铣削加工颤振在线监测研究
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摘要
薄壁件的精加工阶段,由于刀具悬伸长,工件刚度低,加工中容易发生变形进而引起颤振。因此需要可靠的标准监测加工状态,判断加工参数是否合理。首先,采集加工中包含颤振现象的声压数据,分析颤振发生时域有效值及频域功率谱的特点,对比在不同状态的特征,并以这些特征作为监测的依据;然后,在颤振发生时能量集中频段转移,通过小波包分解后构造出反映这一特征的特征量;最后,以小波变换时频图作为状态判断依据,通过离线分析设定相关阈值,设置多重标准,满足时域有效值和频域占能比阈值要求后计算特征值,判断加工状态。验证结果表明,笔者所提出的方法可以准确识别颤振现象,同时表明声压信号可以反映颤振特征。阈值设定后,即可为后续加工在线监测提供判断标准,避免因加工参数选择不合理时对工件或机床造成损害。
In the finishing stage of thin-walled parts,due to the tool overhang,the workpiece stiffness is low,so it is easy to deform during machining and cause flutter.Therefore,a reliable standard is needed to monitor the machining state and determine whether the machining parameters are reasonable.In this study,the sound pressure data including chatter phenomenon are collected and analyzed.The time domain effective value and power spectrum of frequency domain are analyzed.The characteristics of different states are compared,and these characteristics are used as the basis for monitoring.When the flutter occurs,the energy concentration shifts at the frequency band.After the wavelet packet decomposition,the characteristic quantities reflecting the feature are constructed.The wavelet transform time-frequency map is used as the state judgment basis,and the correlation threshold is set up by off-line analysis.After setting multiple standards to meet the requirements of the time domain effective value and the frequency domain energy ratio threshold,the eigenvalues are calculated and the processing status is judged.The flutter phenomenon can be identified accurately,and the acoustic pressure signal can reflect the flutter characteristics.After the threshold setting,the invention can provide a judgment standard for the on-line monitoring of the subsequent processing and avoid the damage to the workpiece or the machine tool due to the unreasonable selection of the processing.
In the finishing stage of thin-walled parts,due to the tool overhang,the workpiece stiffness is low,so it is easy to deform during machining and cause flutter.Therefore,a reliable standard is needed to monitor the machining state and determine whether the machining parameters are reasonable.In this study,the sound pressure data including chatter phenomenon are collected and analyzed.The time domain effective value and power spectrum of frequency domain are analyzed.The characteristics of different states are compared,and these characteristics are used as the basis for monitoring.When the flutter occurs,the energy concentration shifts at the frequency band.After the wavelet packet decomposition,the characteristic quantities reflecting the feature are constructed.The wavelet transform time-frequency map is used as the state judgment basis,and the correlation threshold is set up by off-line analysis.After setting multiple standards to meet the requirements of the time domain effective value and the frequency domain energy ratio threshold,the eigenvalues are calculated and the processing status is judged.The flutter phenomenon can be identified accurately,and the acoustic pressure signal can reflect the flutter characteristics.After the threshold setting,the invention can provide a judgment standard for the on-line monitoring of the subsequent processing and avoid the damage to the workpiece or the machine tool due to the unreasonable selection of the processing.
引文
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[11]Kim S,Lee S Y.Chatter prediction of end milling in a vertical machining center[J].Journal of Sound and Vibration,2001,241(4):567-586.
[12]Delio T,Tlusty J,Smith S.Use of audio signals for chatter detection and control[J].Journal of Engineering for Industry,1992,114(2):146-157.
[13]宋清华.高速铣削稳定性及加工精度研究[D].济南:山东大学,2009.
[2]Li Huaizhong,Jing Xiubing,Wang Jun.Detection and analysis of chatter occurrence in micro-milling process[J].Proceedings of the Institution of Mechanical Engineers,Part B:Journal of Engineering Manufacture,2014,228(11):1359-1371.
[3]Hynynen K M,Ratava J,Lindh T,et al.Chatter detection in turning processes using coherence of acceleration and audio signals[J].Journal of Manufacturing Science and Engineering,2014,136(4):044503.
[4]Quintana G,Ciurana J,Ferrer I,et al.Sound mapping for identification of stability lobe diagrams in milling processes[J].International Journal of Machine Tools and Manufacture,2009,49(3):203-211.
[5]蒋永翔.复杂制造系统加工稳定性在线监测及寻优控制关键技术研究[D].天津:天津大学,2010.
[6]刘晓胜,马玉林.基于电流信号的铣削颤振识别技术研究[J].机械工程学报,2000,36(4):25-29.Liu Xiaosheng,Ma Yulin.Milling chatter recognition technology research based on current electric signal[J].Journal of Mechanical Engineering,2000,36(4):25-29.(in Chinese)
[7]Gradi2ek J,Govekar E,Grabec I.Qualitative and quantitative analysis of stochastic processes based on measured data,II:applications to experimental data[J].Journal of Sound and Vibration,2002,252(3):563-572.
[8]Zhang Zhao,Li Hongguang,Meng Guang,et al.Chatter detection in milling process based on the energy entropy of VMD and WPD[J].International Journal of Machine Tools and Manufacture,2016,108:106-112.
[9]刘强,李忠群.数控铣削加工过程仿真与优化[M].北京:航空工业出版社,2011:20-25.
[10]Weingaertner W L,Schroeter R B,Polli M L,et al.Evaluation of high-speed end-milling dynamic stability through audio signal measurements[J].Journal of Materials Processing Technology,2006,179(1):133-138.
[11]Kim S,Lee S Y.Chatter prediction of end milling in a vertical machining center[J].Journal of Sound and Vibration,2001,241(4):567-586.
[12]Delio T,Tlusty J,Smith S.Use of audio signals for chatter detection and control[J].Journal of Engineering for Industry,1992,114(2):146-157.
[13]宋清华.高速铣削稳定性及加工精度研究[D].济南:山东大学,2009.