Analysis of Metal Cutting Acoustic Emissions by Time Series Models

TL;DR

This paper analyzes acoustic emission signals from metal cutting processes using time series models to understand tool wear and degradation, combining ARMA analysis of continuous signals with point process analysis of bursts.

Contribution

It introduces a combined approach of ARMA modeling and point process analysis to characterize acoustic emissions and tool wear in machining processes.

Findings

Weibull distribution effectively models waiting times between bursts.

ARMA analysis describes the continuous deformation component.

Point process analysis captures discrete burst behavior related to tool wear.

Abstract

We analyse some acoustic emission time series obtained from a lathe machining process. Considering the dynamic evolution of the process we apply two classes of well known stationary stochastic time series models. We apply a preliminary root mean square (RMS) transformation followed by an ARMA analysis; results thereof are mainly related to the description of the continuous part (plastic deformation) of the signal. An analysis of acoustic emission, as some previous works show, may also be performed with the scope of understanding the evolution of the ageing process that causes the degradation of the working tools. Once the importance of the discrete part of the acoustic emission signals (i.e. isolated amplitude bursts) in the ageing process is understood, we apply a stochastic analysis based on point processes waiting times between bursts and to identify a parameter with which to characterise the wear level of the working tool. A Weibull distribution seems to adequately describe the waiting times distribution.