Statistical properties of acoustic emission signals from metal cutting processes

Acoustic emission (AE) data from single point turning machining are analyzed in this paper in order to gain a greater insight of the signal statistical properties for tool condition monitoring applications. A statistical analysis of the time series data amplitude and root mean square (rms) value at various tool wear levels are performed, finding that aging features can be revealed in all cases from the observed experimental histograms. In particular, AE data amplitudes are shown to be distributed with a power-law behavior above a crossover value. An analytic model for the rms values probability density function is obtained resorting to the Jaynes maximum entropy principle; novel technique of constraining the modeling function under few fractional moments, instead of a greater amount of ordinary moments, leads to well-tailored functions for experimental histograms.