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• W4294891718 endingPage "2186" @default.
• W4294891718 startingPage "2161" @default.
• W4294891718 abstract "To improve the finish and efficiency of machining processes, researchers set out to develop techniques to detect, suppress, or avoid vibration chatter. This work involves tracing chatter detection techniques, from time–frequency signal processing methods (FFT, HHT, STFT, etc.), decomposition (WPD, EMD, VMD, etc.) to the combination with machine learning or deep learning models. A cartographic analysis was carried out to discover the limits of these different techniques and to propose possible solutions in perspective to detect chattering in the machining processes. The fact that human expert detects chatter using simple spectrograms is confronted with the variety of signal processing methods used in the literature and lead to possible optimal detecting techniques. For this purpose, the bibliometric tool R-Tool was used to facilitate a bibliometric analysis using specific means for quantitative bibliometric research and visualization. Data were collected from the Web of Science (WoS 2022) using particular queries on chatter detection. Most documents collected detect chatter with either transformation or decomposition techniques." @default.
• W4294891718 created "2022-09-07" @default.
• W4294891718 creator A5026310210 @default.
• W4294891718 creator A5027935882 @default.
• W4294891718 creator A5030041935 @default.
• W4294891718 creator A5076001328 @default.
• W4294891718 date "2022-09-01" @default.
• W4294891718 modified "2023-10-11" @default.
• W4294891718 title "Review of AI-based methods for chatter detection in machining based on bibliometric analysis" @default.
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