  The statistical properties of acoustic emission signals for tool condition
monitoring (TCM) applications in mechanical lathe machining are analyzed in
this paper. Time series data and root mean square (RMS) values at various tool
wear levels are shown to exhibit features that can be put into relation with
ageing in both cases. In particular, the histograms of raw data show power-law
distributions above a cross-over value, in which newer cutting tools exhibit
more numerous larger events compared with more worn-out ones. For practical
purposes, statistics based on RMS values are more feasible, and the analysis of
these also reveals discriminating age-related features. The assumption that
experimental RMS histograms follow a Beta (b) distribution has also been
tested. The residuals of the modeling b functions indicate that the search for
a more appropriate fitting function for the experimental distribution is
desirable.
