Dynamics of stainless steel turning: Analysis by flicker-noise spectroscopy

Abstract

We use flicker-noise spectroscopy (FNS), a phenomenological method for the analysis of time and spatial series operating on structure functions and power spectrum estimates, to identify and study harmful chatter vibrations in a regenerative turning process. The 3D cutting force components experimentally measured during stainless steel turning are analyzed, and the parameters of their stochastic dynamics are estimated. Our analysis shows that the system initially exhibiting regular vibrations associated with spindle rotation becomes unstable to high-frequency noisy oscillations (chatter) at larger cutting depths. We suggest that the chatter may be attributed to frictional stick-and-slip interactions between the contact surfaces of cutting tool and workpiece. We compare our findings with previously reported results obtained by statistical, recurrence, multifractal, and wavelet methods. We discuss the potential of FNS in monitoring the turning process in manufacturing practice.

Analysis of EEG signal by flicker-noise spectroscopy: identification of right-/left-hand movement imagination

“…The FNS is a time-series analysis method that introduces parameters characterizing the components of stochastic signals in different frequency ranges [59]. The method has found numerous applications; among others it is worth mentioning about the use of FNS to the parameterization of images produced by the atomic force microscopy (AFM) [51], analysis of geological signals measured in seismic areas [15, 49], determination of electric breakdowns precursors in thin porous silicon films [39], analysis of electric potential fluctuations in electromembrane systems [58] or monitoring cutting processes and development of stability maps for materials [29]. The FNS method was also successfully applied to some problems in a medical data processing.…”

Stabilizing wave attenuation effects in turning process

“…Damped natural frequency has been experimentally confirmed to exist in the frequency response spectrum of high-speed milling (Insperger, Stepan, Bayly, & Mann, 2003). Experimental data of chatter and other vibrations in the turning and milling processes have been characterized using multi-scale methods (Litak, Syta, & Rusinek, 2011;Sen, Litak, Syta, & Rusinek, 2013) and flicker-noise spectroscopy (Litak, Polyakov, Timashev, & Rusinek, 2013). The theory of stabilizing wave attenuation effects in machining process was presented by Ozoegwu and Omenyi (2013).…”

Dynamics of thin-walled element milling expressed by recurrence analysis

“…Although, experimental results are very often compared with FEM results [3,17], the problem of experimental signal analysis remains an open question. Different methods of signal analysis are applied in order to recognize chatter vibrations in cutting operations, including multifractal and wavelet approaches [38], multiscale entropy [23] HilbertHuang transform [20], recurrence analysis [22], flicker-noise spectroscopy [21] and audio signal analysis [32]. As it was mentioned before, the HSM method is also applied to thin-walled aluminium components for the aircraft industry to increase productivity [8].…”