Using detrended fluctuation analysis to monitor chattering in cutter tool machines

Abstract This paper considers accelerometer signals in order to detect chatter instabilities under different spindle speed and depth of cut ratio conditions. Detrended fluctuation analysis (DFA), adapted for time–frequency domain, was used to monitor the evolution of cutter tool dynamics. The DFA offers the advantage over traditional spectral analysis that can be deal with nonstationary, nonlinear data and, in contrast to wavelet approaches, its application does not rely on the selection of basis functions. The underlying idea behind the application is to use the Hurst exponent, an index of the signal fractal roughness, to detect dominance of unstable oscillatory components in the complex, presumably stochastic, dynamics of machine acceleration. Several experiments with a lab-scale cutting machine were performed to illustrate the ability of the DFA to detect unstable cutting behavior. The results, presented in time–frequency domain, show that instabilities are detected in a certain frequency range as the Hurst exponent decreases to reflect anti-persistency of the chatter dynamics.