Detection of arc characteristics and weld forming quality of aluminum alloy DP-MIG welding using AE signal through resonance demodulation

He, Kuanfang; Xia, Zixiong; Si, Yin; Liang, Jiahe; Yong, Jiangfeng; Shi, Wenqing

doi:10.1016/j.measurement.2021.110427

Cited by 10 publications

(1 citation statement)

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“…For the analysis of AE signals, different feature extraction method mainly including time domain analysis, frequency domain analysis and time-frequency analysis were reported in literature. [20][21][22] Generally, the RMS of AE signal was used to characterize energy, the typical frequency characteristics could be obtained by Fourier transform of time domain signals, and the short-time Fourier transform was often used in time-frequency analysis. 23 The effectiveness of AE monitoring for drilling glass fiber reinforced plastic laminate via time and frequency domain was investigated by Ravishankar and Murthy.…”

Section: Introductionmentioning

confidence: 99%

Surface defect and chatter monitoring in robotic drilling CFRP composites using acoustic emission technique

Jiang

Huang

2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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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.

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