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Chatter as a common and thorny problem occurs easily during robotic milling process, leading to the instability, severe tool wear and poor surface finish. In this work, an acoustic emission technique was employed to analyze a chatter phenomenon using root mean square (RMS) value and fast Fourier transform method during high-speed robotic milling of aluminum alloys (with cutting speed up to 678 m/min). A stability lobe diagram was proposed to predict the occurrence of chatter with various spindle speeds, which was considered as the most effective tool for chatter analysis. The underline mechanism and theoretical analysis were also presented to provide physical understanding of chatter stability. The cutting force model and robot structure model were firstly established to study chatter mechanism. The stability of a robotic milling system was then analyzed using a zero-order approximation method. Results showed that fast Fourier transform and the time-domain root mean square (RMS) value of acoustic emission signals could be effectively used for detection and verification of chatter in the robotic milling process. The stable cutting zone in the stability lobe diagram was in agreement with experimental results, which can help for the selection of reasonable cutting parameters to avoid chatter and improve efficiency during the high-speed robotic milling process.

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Robotic machining is feasible for cutting composites in the field of aviation manufacturing with the benefit of high flexibility and applicability. Riveting is widely used as an important joining technique of aircraft structures requiring hole making on CFRP composites. However, due to the inhomogeneous and anisotropy characteristics of CFRP composites and the weak rigidity of robot, surface damage, and chatter are prone to occur during robotic drilling process, which significantly affect geometrical accuracy of assembled structure. In this work, robotic drilling trials on CFRP composites with different parameters (spindle speed: 3000–15000 rpm, feed rate: 0.01–0.10 mm/rev) were conducted to understand the formation mechanisms of surface damage, as well as the relationship between acoustic emission (AE) signals and defect characteristics. The root mean square (RMS), fast Fourier transform and short-time Fourier transform were successfully used for processing AE signals. The occurrence of entry burr and exit burr were investigated based on different fiber cutting angle and corresponding fiber fracture mechanisms. Drilling status and detailed mechanisms of indentation during robotic drilling were identified/monitored by processing AE signals in the time domain, frequency domain, and time-frequency domain analysis. Matrix cracking, delamination and fiber failure during robotic drilling process was highly related with AE frequency of 0–129, 133–203 and 221–346 kHz, respectively.