基于均方频率与EMD的切削颤振特征提取方法  

Cutting chatter feature extraction method based on mean square frequency and Empirical Mode Decomposition

作　　者：邢诺贝 刘福军 周超 许黎明[1] XING Nuobei;LIU Fujun;ZHOU Chao;XU Liming(School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240,CHN)

机构地区：[1]上海交通大学机械与动力工程学院,上海200240

出　　处：《制造技术与机床》2021年第3期35-40,共6页Manufacturing Technology & Machine Tool

基　　金：国家科技重大专项(2019ZX04027-001)。

摘　　要：颤振对机床加工稳定性影响极大,严重制约机床加工效果和效率。为实现切削颤振的在线监测与预报,提出了一种基于均方频率与经验模态分解的颤振特征提取方法。通过对切削力信号进行频域分析,计算小波包颤振特征频带的均方频率占比系数作为特征T_(1);通过计算IMF分量与原信号的相关性,选择相关性强的IMF并计算能量熵作为特征T_(2)。研究结果表明,该两种方法都能够有效识别颤振。T_(1)总体变化率较小,达到20%,但能够更快地实现颤振早期预警。T_(2)在早期预警上略有不足,但其总体变化率较大,接近80%,能实现准确报警,与T_(1)形成有效的互补,实现对切削颤振的预报警。Chattering has a great influence on the stability of machine tool processing,which seriously restricts the result&efficiency of machine tool processing.In order to realize online monitoring and prediction of cutting chatter,a method for extracting chatter characteristics based on mean square frequency and empirical mode decomposition is proposed.Through frequency domain analysis of the cutting force signal,the mean square frequency ratio coefficient of the wavelet packet flutter characteristic frequency band is calculated as the characteristic T_(1);By calculating the correlation between the IMF component and the original signal,the IMF with strong correlation is selected and the energy entropy is calculated as the characteristic T_(2).The research results show that both methods can effectively identify chatter.The overall rate of change of T_(1)is small,reaching 20%,but the early warning of flutter can be achieved faster.T_(2)is slightly inadequate in early warning,but its overall rate of change is relatively large,close to 80%,which can achieve accurate alarms,form an effective complement to T_(1),and achieve pre-alarms for cutting chatter.

关 键 词：切削颤振 均方频率 小波包分解 特征提取 

分 类 号：TH113[机械工程—机械设计及理论] TP181[自动化与计算机技术—控制理论与控制工程]