Regenerative chatter identification in grinding using instantaneous nonlinearity indicator of servomotor current signal. Issue 1 (March 2017)

• Record Type: Journal Article
• Authors: Li, Yong; Zhou, Shaoping; Lin, Jing; Wang, Xiufeng
• Is Part Of: International journal of advanced manufacturing technology. Volume 89:Issue 1/4(2017)
• Page Start: 779
• Page End: 790
• Publication Date: 2017-03
• Subjects: Regenerative chatter in grinding -- Current signature -- Non-stationary wavelet bicoherence -- Instantaneous nonlinearity indicator -- Continuous wavelet transform; Manufacturing processes -- Periodicals; Production engineering -- Periodicals; 670.427
• DOI: 10.1007/s00170-016-9117-z
• Languages: English
• ISSNs: 0268-3768

Abstract

Abstract Chatter is considered as one of the most important causes of instability in the precision grinding process. Suffering from the influence of nonlinearity and changed machining condition, regenerative chatter signal is nonlinear and non-stationary in nature, which makes the linear representations and stationary assumed methods not appropriate. Wavelet bicoherence is considered as an effective method for these nonlinear signals analysis. However, current wavelet bicoherence is often estimated by integrating over the finite time interval. This procedure may result to the loss of the time information; hence, it is not appropriate for chatter signal that cannot be assumed to be stationary. In this paper, an instantaneous nonlinearity indicator-based method is proposed for regenerative chatter identification in grinding by using the servomotor current signal. Firstly, the nonlinearity of servomotor current affected by regenerative chatter is discussed. After that, a non-stationary wavelet bicoherence is proposed for the quadratic nonlinear coupling detection of these nonlinear and non-stationary signals. On this basis, an instantaneous nonlinearity indicator is further established for regenerative chatter identification. The effectiveness of the proposed method is verified by simulations and experiments, and the results show that, compared with the commonly used wavelet bicoherence and continuous wavelet transform, this method can not only describe the non-stationary chatter signal in time-frequency domain but also extract the true nonlinear coupling component resulted from regenerative chatter.