颤振是影响机床加工质量的重要原因之一。为实现切削颤振的实时在线识别与评价，采用加速度传感器，获取主轴振动信号，以小波包能量熵值为指标，对铣削加工的稳定状态及振动形式进行识别。通过多传感器对加工过程进行监测，确定加工的稳定性；对主轴振动信号进行频谱分析，了解不同加工状态下的信号频谱特点，分析其振动形式。对信号进行小波包分解，发现在不同的振动状态下，信号的能量分布有显著规律。试验表明，切削从稳定状态到不稳定状态，本质上是强迫振动和颤振的能量强度和分布发生了变化。能量熵描述能量分布的变化，是识别切削状态和振动状态变化的有效方法。
Chatter is one of the critical factors that hinder machining quality in machining processes. To identify and evaluate chatter online, the acceleration is used to obtain the vibration signal of spindle. And by using the energy entropy of wavelet packet decomposition, the vibration signal is decomposed to judge the stability of milling and distinguish the vibration states. The stability of milling can be determined by the multi-sensor. Understanding the difference of signal in different conditions and analyzing the vibration forms the spectrum analysis is the second step. Then decomposition of the vibration signal with wavelet packet presents a significant law in the energy distribution of signal in different vibration forms. Several milling tests are conducted and shows that the milling state changing from stability to instability is same to the changing energy intensity between forced vibration and chatter. The energy entropy can describe the different distribution of energy well, which is a good way to identify the milling state and the vibration states.
[1] TOBIAS S A,FISHWICK W. Theory of regenerative machine tool chatter[J]. The Engineer,1958,205(7):199-203.
[2] TAYLOR F W. On the art of cutting metals[J]. Transactions of ASME,1907,28:101-108.
[3] QUINTANA G,CIURANA J. Chatter in machining processes:A review[J]. International Journal of Machine Tools & Manufacture,2011,51:363-376.
[4] SIDDHPURA M,PAUROBALLY R. A review of chatter vibration research in turning[J]. International Journal of Machine Tools & Manufacture,2012,61:27-47.
[5] ZHU Kunpeng,WONG Y S,HONG G S. Wavelet analysis of sensor signals for tool condition monitoring:A review and some new results[J]. International Journal of Machine Tools & Manufacture,2009,49:537-553.
[6] ALTINTAS Y,WECK M. Chatter stability of metal cutting and grinding[J]. CIRP Annals-Manufacturing Technology,2004,53(2):619-642.
[7] SCHMITZ T L,MEDICUS K,DUTTERER B. Exploring once-per-revolution audio signal variance as a chatter indicator[J]. Machining Science and Technology,2002,6:215-233.
[8] TANGJITSITCHAROEN S,PONGSATHORNWIWAT H N. Development of chatter detection in milling processes[J]. International Journal of Machine Tools & Manufacture,2013,65:919-927.
[9] ALTINTAS Y,EYNIAN M,ONOZUKA H,et al. Identification of dynamic cutting force coefficients and chatter stability with process damping[J]. CIRP Annals-Manufacturing Technology,2008,57:371-374.
[10] YAO Zhehe,MEI Deqing,CHEN Zichen. On-line chatter detection and identification based on wavelet and support vector machine[J]. Journal of Materials Processing Technology,2010,210:713-719.
[11] DELIO T,TLUSTY J,SMITH S. Use of audio signals for chatter detection and control[J]. Journal of Engineering for Industry,1992,114:146-157.
[12] TSAI N C,CHEN D C,LEE R M. Chatter prevention for milling process by acoustic signal feedback[J]. International Journal of Machine Tools & Manufacture,2010,47:1013-1021.
[13] YANG Yu,YU Dejie,CHENG Junsheng. A roller bearing fault diagnosis method based on EMD energy entropy and ANN[J]. Journal of Sound and Vibration,2006,294:269-277.
[14] ZHANG Chunliang,YUE Xia,JIANG Yongtao,et al. A hybrid approach of ANN and HMM for cutting chatter monitoring[J]. Advanced Materials Research,2010,97:3225-3232.
[15] 黄强,张根保,张新玉,等. 对再生型切削颤振模型的试验分析[J]. 振动工程学报,2008,21(6):547-552. HUANG Qiang,ZHANG Genbao,ZHANG Xinyu,et al. Experimental analysis on regenerative chatter model[J]. Journal of Vibration Engineering,2008,21(6):547-552.
[16] ZHANG Zhao,LIN 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 & Manufacture,2016,108:106-112.
[17] ABUZAHRA N,LANGE J. Tool chatter monitoring in turning operations using wavelet analysis of ultrasound waves[J]. International Journal of Machine Tools & Manufacture,2002,20:248-254.
[18] KWAK J. Application of wavelet transform technique to detect tool failure in turning operations[J]. International Journal of Machine Tools & Manufacture,2006,28:1078-1083.
[19] HUANG Jian,HU Xiaoguang,GENG Xin. An intelligent fault diagnosis method of high voltage circuit breaker based on improved EMD energy entropy and multiclass support vector machine[J]. Electric Power Systems Research,2011,81:400-407.
