首页|Studies from Shanghai Jiao Tong University Further Understanding of Robotics (A Chatter Recognition Approach for Robotic Drilling System Based On Synchroextracting Chirplet Transform)
Studies from Shanghai Jiao Tong University Further Understanding of Robotics (A Chatter Recognition Approach for Robotic Drilling System Based On Synchroextracting Chirplet Transform)
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NETL
NSTL
IEEE
Data detailed on Robotics have been presented. According to news reporting originating from Shanghai, People's Republic of China, by NewsRx correspondents, research stated, “Chatter is one of the main barriers that significantly limits the efficiency and quality of robotic drilling processes. In this work, we present an approach based on synchroextracting chirplet transform (SECT) for early chatter recognition in a robotic drilling system.” Funders for this research include National Natural Science Foundation of China (NSFC), Natural Science Foundation of Shanghai, Shanghai Municipal Science and Technology Major Project. Our news editors obtained a quote from the research from Shanghai Jiao Tong University, “It mainly comprises four steps: signal preprocessing, time-frequency analysis (TFA), signal reconstruction, and indicator calculation. To remove the disturbances of the rotation-related frequencies, matrix notch filters are designed to preprocess the acquired vibration signal. The nonstationary and nonlinear properties of chatter acceleration signal are characterized by the SECT, which extracts the time-frequency (TF) points satisfying the instantaneous frequency (IF) equation to acquire an energy-concentrated TF representation. The whole signal is decomposed into several subsignals, and reconstruction for the SECT is then employed to reconstruct each subsignal for different frequency bands. To characterize signal energy and frequency distribution change, the energy entropy is selected as an indicator for chatter monitoring. The effectiveness and superiority of the presented recognition approach were verified by robotic drilling tests under various cutting parameters and part materials. The results demonstrated that the presented chatter recognition approach could identify the onset of robotic drilling chatter effectively and timely. Moreover, it detected the chatter 58.7 and 136.8 ms earlier on average than the synchroextracting-based and multisynchrosqueezing-based methods, respectively.”
ShanghaiPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningRoboticsRobotsShanghai Jiao Tong University
NETL
NSTL
IEEE
