Identification of dynamic instabilities in machining process using the approximate entropy method

Chatter instability is a persistent problem in machining process that produces vibrations characterized by nonlinear and nonstationary dynamics. Although traditional fast Fourier transform approaches are typically used for the monitoring of chattering in industry, the method is suitable only for linear and stationary signals. In this paper, methods based on approximate entropy (ApEn) are proposed to identify chatter instabilities in milling process. as entropy is an index of the irregularity and complexity related to randomness from data series. The attractiveness of the ApEn approach is that it can deal with nonlinear and nonstationary data, requires a relatively small number of observations and can be used for noisy signals. For a lab-scale milling experimental setup, the ApEn was implemented under a time–frequency monitoring method, showing that instable chattering is associated with entropy increment for a frequency range. In contrast, stable milling led to an entropy pattern where high-entropy values are concentrated at high frequencies, which are related to the natural dynamics of the cutting tool.