Statistical properties of acoustic emission signals from metal cutting processes
Abstract
Acoustic Emission (AE) data from single point turning machining are analysed in this paper in order to gain a greater insight of the signal statistical properties for Tool Condition Monitoring (TCM) applications. A statistical analysis of the time series data amplitude and root mean square (RMS) value at various tool wear levels are performed, �nding that ageing 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 behaviour above a cross-over value. An analytic model for the RMS values probability density function (pdf) is obtained resorting to the Jaynes' maximum entropy principle (MEp); novel technique of constraining the modelling function under few fractional moments, instead of a greater amount of ordinary moments, leads to well-tailored functions for experimental histograms.
Keywords
Cite
@article{arxiv.physics/0404128,
title = {Statistical properties of acoustic emission signals from metal cutting processes},
author = {F. A. Farrelly and A. Petri and L. Pitolli and G. Pontuale and A. Tagliani and P. L. Novi Inverardi},
journal= {arXiv preprint arXiv:physics/0404128},
year = {2009}
}
Comments
16 pages, 7 figures