Application of the Empirical Mode Decomposition method for the prediction of the tool wear in turning operation
Tool wear is one of the factors to consider since its evolution damages and degrades the surface roughness of machined material. For this raison, this article proposes the application of a new time-frequency method, called Empirical Mode Decomposition, for the prediction of the tool wear in turning operation.
The proposed method is applied on vibratory acceleration signals measured during the cutting process in different configurations. The sifting process of the EMD method decomposes the measured signal into several Intrinsic Modes Functions allowing its time-frequency analysis. A time and frequency domain analyses have been carried out.
In time domain, the tool wear monitoring is performed by using two scalar indicators; the energy and the mean power of the first IMF of the EMD decomposition. The variation of these indicators over the entire tool life highlights the three periods and allows locating the transition point between the wear stabilization and the wear acceleration period. The prediction of the tool ageing is then very clear avoiding its collapse and the machining fail.
In the frequency domain a new indicator, based on the amplitude of the peak corresponding to the tool’s natural frequency, is proposed. It has been shown that the variation of this indicator over the entire tool life is the same as for the scalar indicators. The three periods are shown and the critical transition point reflecting the beginning of the tool ageing is clearly detectable.
The experimental results are very promising in industrial environment for the implementation of online monitoring system for cutting tool state.