作者:\n\t王瀚彬，李茂月，刘献礼，王志学，孟博洋\n

Authors:\n\tWANG Hanbin,LI Maoyue,LIU Xianli,WANG Zhixue,MENG Boyang\n
摘要:\n\t在高速铣削航空零件时，由于薄壁结构刚度较低，容易产生颤振，颤振导致表面质量差，尺寸误差，降低刀具和机器寿命，是性能的主要限制之一。因此，需要一种可靠的检测方法来识别颤振。针对薄壁结构铣削过程中的颤振检测问题，提出一种基于优化变分模态分解和多尺度样本熵的薄壁件颤振特征提取方法。首先，为了解决变分模态分解参数选择问题，提出一种基于遗传算法优化和最小排列熵的参数自适应方法。其次，计算分解信号的能量比作为挑选IMFs的原则，从而进行信号重构。为了解决单尺度样本熵不能很好地反映颤振发生时铣削力信号特征，引入多尺度样本熵对铣削颤振进行检测，并进行了实验验证。结果表明，采用优化变分模态分解算法对信号进行处理，可以避免因模态混叠而造成的颤振信号难以分离的问题。多尺度样本熵比单尺度样本熵更加有利于颤振检测，随着尺度因子的增大，铣削信号的MSE有减小的趋势，且尺度因子为10时的MSE更有利于颤振检测。\n

Abstract:\n\tIn the high-speed milling of aviation parts, due to the low stiffness of thin-walled structure, it is easy to produce chatter.Chatter leads to poor surface quality, dimensional error and reducing the service life of tools and machines.Therefore, a reliable detection method is needed to identify chatter.Aiming at the problem of chatter detection in the milling process of thin-walled structures, a chatter feature extraction method of thin-walled parts based on optimal variational mode decomposition and multi-scale sample entropy is proposed.Firstly, in order to solve the problem of parameter selection in variational modal decomposition, a parameter adaptive method based on genetic algorithm optimization and minimum permutation entropy is proposed.Then, the energy ratio of the decomposed signal is calculated as the principle of selecting IMF, so as to reconstruct the signal.In order to solve the problem that singlescale sample entropy can not well reflect the characteristics of milling force signal when chatter occurs, multi-scale sample entropy is introduced to detect milling chatter.Finally, the experimental results show that the optimal variational modal decomposition algorithm can avoid the problem of difficult separation of chatter signals caused by modal aliasing.Multi-scale sample entropy is more conducive to chatter detection than singlescale sample entropy. MSE of milling signal tends to decrease with the increase of scale factor, and MSE with scale factor of 10 is more conducive to chatter detection.\n


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