Real-time chatter detection and automatic suppression for intelligent spindles based on wavelet packet energy entropy and local outlier factor algorithm

In this research, we proposed a real-time chatter detection and suppression module for intelligent spindle to increase machining efficiency and processing yield. For early detections of chatters, the relative wavelet packet energy entropy with high sensitivity in the high-frequency band and the loca...

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Bibliographic Details
Published inInternational journal of advanced manufacturing technology Vol. 103; no. 1-4; pp. 297 - 309
Main Authors Yao, Yung-Chen, Chen, Yu-Hsuan, Liu, Chien-Hao, Shih, Wen-Pin
Format Journal Article
LanguageEnglish
Published London Springer London 01.07.2019
Springer Nature B.V
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ISSN0268-3768
1433-3015
DOI10.1007/s00170-019-03551-2

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Summary:In this research, we proposed a real-time chatter detection and suppression module for intelligent spindle to increase machining efficiency and processing yield. For early detections of chatters, the relative wavelet packet energy entropy with high sensitivity in the high-frequency band and the local outlier factor (LOF) algorithm were utilized as chatter features and classifications, respectively. Based on the pre-obtained three-dimensional stability lobe diagram (SLD) and a LOF-based trained model, the module could real-time monitor and suppress chatter during machining processes. The module was implemented and experimentally examined with the CNC end-milling machine under five different cutting conditions for verifying the capabilities of real-time chatter identifications and suppressions. It was demonstrated that the module could detect the onset of chatter and suppress it by changing the cutting conditions to avoid damages on the surfaces of working piece due to severe chatters.
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ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-019-03551-2