关键词: 机器人铣削, 颤振识别, 特征提取, 智能模型

Abstract:  Aiming at the problems that the form of robot milling chatters was complex and difficult to identify effectively, a robot milling chatter adaptive identification method was proposed. Firstly, to retain the information that could characterize the robotic milling states, the original vibration signals were separated. Then the difference between the power spectrum entropy was used to characterize the frequency distribution characteristics of vibration signals in different milling states, and the standard deviation of the original signals was used to characterize the time domain characteristics of vibration signals in robotic milling. The three-dimensional feature vector was input to the support vector machine to construct the adaptive recognition model of robotic milling chatters. The model was verified by experiments. The results show that the proposed adaptive chatter recognition model may accurately identify regenerative chatters and low-frequency chatters in robotic milling, and the recognition accuracy of stable, early regenerative chatters, severe regenerative chatters, low frequency chatters and no-load state reaches 93%, which is better than existing methods.

Key words:  , robotic milling, chatter recognition, feature extraction, intelligent model