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

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stability information both in time domain and frequency domain, and the common analytical transformation methods are no longer applicable.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.