![]() Lajmert P, Rusinek R, Kruszyński B (2018) Chatter identification in milling of Inconel 625 based on recurrence plot technique and Hilbert vibration decomposition. Ye J, Feng P, Xu C, Ma Y, Huang S (2018) A novel approach for chatter online monitoring using coefficient of variation in machining process. Ĭaliskan H, Kilic ZM, Altintas Y (2018) On-Line Energy-Based Milling Chatter Detection. Hynynen KM, Ratava J, Lindh T et al (2014) Chatter Detection in Turning Processes Using Coherence of Acceleration and Audio Signals. Liu H, Chen Q, Li B et al (2011) On-line chatter detection using servo motor current signal in turning. Wang Y, Bo Q, Liu H, Hu L, Zhang H (2018) Mirror milling chatter identification using Q-factor and SVM. IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 1052–1057. ĭing L, Sun Y, Xiong Z (2017) Early chatter detection based on logistic regression with time and frequency domain features. Ji Y, Wang X, Liu Z et al (2018) Early milling chatter identification by improved empirical mode decomposition and multi-indicator synthetic evaluation. Yang K, Wang G, Dong Y et al (2019) Early chatter identification based on an optimized variational mode decomposition. Quintana G, Ciurana J (2011) Chatter in machining processes : A review. Also, the proposed indicator is compared with the commonly used Shannon entropy-based indicator and verified to have a larger difference between the stable and chatter statuses and is higher sensitivity to the chatter.īudak E, Altintaş Y (1998) Analytical Prediction of Chatter Stability in Milling-Part II: Application of the General Formulation to Common Milling Systems. The results show that the value of the proposed indicator changes sharply at the onset of chatter in various milling conditions with different spindle speeds and cutting depths. Various milling experiments are conducted. In order to eliminate the interference of the normal signal components, i.e., the spindle speed-related frequency components, the spectrum is preprocessed to filter out those components first. ![]() As a result, the value of the Rényi entropy-based indicator decreases rapidly at the onset of the chatter. As the chatter severity level grows, the signal components at the chatter frequencies become more and more significant, which means a reduction of the randomness of the spectral series. ![]() ![]() Since the spectrum of the chatter signal exhibits discrete spectral lines around the chatter frequencies and the Rényi entropy is an effective index to characterize the randomness of data series, the frequency-domain Rényi entropy is proposed as a chatter indicator. This paper presents a practical method to identify the chatter with cutting force signals in milling processes. Chatter is a kind of self-excited vibration and causes negative effects in machining processes. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |