Chatter detection and stability region acquisition in thin-walled workpiece milling based on CMWT

ORIGINAL ARTICLE

Abstract

Milling chatter is one of the biggest obstacles to achieve high performance machining operations of thin-walled workpiece in industry field. In the milling process, the time-varying and position-dependent characteristics of thin-walled components are evident. So, effective identification of modal parameters and chatter monitoring are crucial. Although the advantage of chatter monitoring by sound signals is obvious, the milling sound signals are nonstationary signals which contain more stability information both in time domain and frequency domain, and the common analytical transformation methods are no longer applicable. In this paper, short time Fourier transform (STFT) is taken as an example to compare the processing results with cmor continuous wavelet transform (CMWT). This article concerns the chatter detection and stability region acquisition in thin-walled workpiece milling based on CMWT. CMWT combines the advantages of the cmor wavelet and continuous wavelet transform which has good locality and the optimal time-frequency resolution. Therefore, CMWT can be adaptively adjusted signal by the window, which is very suitable for processing nonstationary milling signals. Firstly, the model and characteristics of thin-walled workpiece during the cutting process are presented. Secondly, the CMWT method for chatter detection based on acoustic signals in thin-walled component milling process is presented. And the chatter detection results and stability region acquisitions are analyzed and discussed through a specific thin-walled part milling process. Finally, the accuracy of the method presented is verified through the traditional stability lobe diagram predicted using the exiting numerical method and the machined surface morphologies at different cutting positions obtained through the confocal laser microscope.

Keywords

Chatter detection Thin-walled components Cmor continuous wavelet transform Acoustic signal 

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Notes

Acknowledgments

The authors are grateful to the financial supports of the National Natural Science Foundation of China (no. 51575319), Young Scholars Program of Shandong University (no. 2015WLJH31), the United Fund of Ministry of Education for Equipment Pre-research (no. 6141A02022116), and the Key Research and Development Plan of Shandong Province (no. 2018GGX103007).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical EngineeringShandong UniversityJinanPeople’s Republic of China
  2. 2.National Demonstration Center for Experimental Mechanical Engineering EducationShandong UniversityJinanPeople’s Republic of China

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