Intelligent chatter detection using image features and support vector machine

  • Yun Chen
  • Huaizhong Li
  • Xiubing Jing
  • Liang HouEmail author
  • Xiangjian Bu


Chatter is a self-excited vibration that affects the part quality and tool life in the machining process. This paper introduces an intelligent chatter detection method based on image features and the support vector machine. In order to reduce the background noise and highlight chatter characteristics, the average FFT is applied to identify the dominant frequency bands that divide the time-frequency image of the short-time Fourier transform into several sub-images. The non-stationary properties of the machining conditions are quantified using sub-images features. The area under the receiver operating characteristics curve ranks the extracted image features according to their separability capabilities. The support vector machine is designed to automatically classify the machining conditions and select the best feature subset based on the ranked features. The proposed method is verified by using dry micro-milling tests of steel 1040 and high classification accuracies for both the stable and unstable tests are obtained. In addition, the proposed method is compared with two additional methods using either image features from the continuous wavelet transform or time-domain features. The results present a better classification performance than the two additional methods, indicating the efficiency of the proposed method for chatter detection.


Micro-milling Chatter detection Image features Dominant frequency bands Support vector machine 


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This study was jointly supported by the Collaborative Innovation Center of High-End Equipment Manufacturing in Fujian and International Postdocs Exchange Program. The authors would like to express their acknowledgements to the Advanced Manufacturing Laboratory, UNSW, for the support of the experimental work. Comments and suggestions from reviewers are greatly appreciated.


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© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mechanical and Electrical EngineeringXiamen UniversityXiamenChina
  2. 2.Griffith School of Engineering, Gold Coast CampusGriffith UniversityBrisbaneAustralia
  3. 3.Key Laboratory of Mechanism Theory & Equipment Design of ministry of educationTianjin UniversityTianjinChina

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