EEMD-based online milling chatter detection by fractal dimension and power spectral entropy
EEMD-based online milling chatter detection by fractal dimension and power spectral entropy
复制标题
基于 EEMD 的分形维数和功率谱熵在线铣削颤振检测
DOI：
10.1007/s00170-017-0183-7
复制
发表时间：
2017-09-01
影响因子：
3.4
通讯作者：
Wang, Junqing
中科院分区：
文献类型：
--
作者：
Ji, Yongjian;Wang, Xibin;Wang, Junqing
颤振是机械加工过程中产生的一种自激不稳定振动，它会导致加工表面质量差、尺寸精度误差、噪声过大、刀具磨损等多种负面影响。为了实时监测铣削过程的加工状态，及时检测颤振，提出了一种新的在线颤振检测方法。该方法利用集成经验模态分解（EEMD）自适应分析方法将传感器采集的加速度信号分解为一系列固有模态函数（IMF），并选取包含铣削过程特征信息的IMF作为分析信号。引入功率谱熵和分形维数两个指标，通过形态学覆盖方法得到颤振特征。这两个指标可以同时反映提取信号的频率特征和形态特征。为验证该方法的有效性，进行了铣削加工实验，实验结果表明，该方法能及时有效地检测出颤振，对提高铣削加工质量具有重要意义。最后，为了实时检测铣削颤振，开发了铣削颤振在线监测系统。
Chatter is a kind of self-excited unstable vibration during machining process, which always leads to multiple negative effects such as poor surface quality, dimension accuracy error, excessive noise, and tool wear. For purposes of monitoring the processing state of milling process and detecting chatter timely, a novel online chatter detection method was proposed. In the proposed method, the acceleration signals acquired by sensor were decomposed into a series of intrinsic mode functions (IMFs) by the adaptive analysis method named ensemble empirical mode decomposition (EEMD), and the IMFs which contain the feature information of milling process were selected as the analyzed signals. The two indicators power spectral entropy and fractal dimension which is obtained by morphological covering method are introduced to detect the chatter features. Then, both the frequency characteristic and morphological feature of the extracted signals can be reflected by the two indicators. To verify the approach, milling experiments were performed; the experiment results show that the proposed method can detect chatter timely and effectively, which is important in the aspect of improving the milling quality. And finally, in order to detect milling chatter timely, an online milling chatter monitoring system was developed.