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Acoustic emission (AE) refers to the elastic stress waves
generated at a source by some sudden release of strain energy
within a material due to a rearrangement of its internal structure
\cite{spanner74,kannatey-asibu81}. In metal cutting, there are two
major processes that produce changes in the internal structure of
the work material: dislocation motion, which results in plastic
deformation, and fracture. It has been
proposed~\cite{kannatey-asibu81,moriwaki83} that the sources that
contribute to AE generation during metal cutting are the following
(Figure~\ref{fig:ae_zones}):
\begin{itemize}
   \item Plastic deformation in the primary and secondary zones.
   \item Tool-chip and tool-work sliding friction in the secondary
   and tertiary zones respectively.
   \item Colision, entangling and fracture of chips.
   \item Fracture of the cutting tool.
\end{itemize}
\begin{figure}[h]
   \center{\includegraphics{ae_zones.eps}}
   \caption[Sources of AE in metal cutting.]
   {Sources of AE in metal cutting. (a)~Continuous chip formation.
   (b)~Discontinuous chip formation.}
   \label{fig:ae_zones}
\end{figure}
Moreover, the AE emissions have been reported to be dependent on
work and tool materials, tool geometry, cutting speed, feed rate,
width of cut and cutting fluids.

The stress waves generated at the emission source propagate
through the material at roughly the speed of sound as shear and
pressure waves. Surface wave AE propagation is also possible. As
the stress waves propagate, they are attenuated, reflected and
distorted. At the surface of the material the amplitude of the AE
vibrations is very small (usually a few nanometres) and the
frequency content may extend from the audible frequency zone to
the megahertz range. However, in metal cutting, the frequency
range of the signals normally utilized is in the order of
\mbox{$10^2$--$10^3$ kHz}.

Acoustic emission can be detected with an appropriate transducer,
coupled to the surface of the material, that, when stimulated by
the AE stress waves, transforms the mechanical excitations into
electrical signals. Usually, AE transducers are constructed of a
piezoelectric ceramic element, lead zirconate titanate (PZT).
Figure~\ref{fig:pzt_transducer} shows a conventional PZT
transducer.
\begin{figure}[h]
   \center{\includegraphics{pzt_transducer.eps}}
   \caption{Piezoelectric transducer.}
   \label{fig:pzt_transducer}
\end{figure}
The detected signals are then amplified, filtered, and transmitted
to equipment that stores and analyses the incoming signals. In
metal cutting, common amplification factors range from
$20$--$60$~dB, and band-pass filters are usually employed in the
$10^2$--$10^3$~kHz range. Figure~\ref{fig:ae_process_diagram}
shows the various stages of the AE process, starting from the
emission source to the final stages of signal processing and
analysis.


Due to the randomness of the AE signals, the analysis and
interpretation of AE is normally quite complex. Therefore, signal
processing techniques are normally needed to process the raw AE
signals. The principal processing techniques applied to AE
generated in metal cutting are described next:

\begin{description}

   \item [Energy representation] The most common way to represent
   the energy of a signal is by calculating its root mean square
   (RMS). This is done by taking the square root of the mean of
   the square of the signal time series. Another method makes use
   of the previously filtered and full-wave rectified signal that
   is then used to compute a probability density function and to
   compute the maximum value of the distribution function, the mode.
   The mode is known to be a statistical averaging technique that
   is less sensitive to outlying values than the mean.

   \item [Count techniques] There are two major techniques: total
   counts ($N$) and count rate ($\dot N$). The total counts is the
   number of times a signal overcomes a given threshold. The
   number of counts per unit time is the count rate. The
   dependence of the count techniques on a threshold level can lead
   to problems since if the signal amplitude changes, the count
   becomes meaningless if the threshold in also not changed.

   \item [Spectra analysis] When a signal is sampled, it is
   normally represented as a series of amplitudes. The signal is
   said to be represented in the time domain, i.e.\ the signal is
   represented as a function of time. The signal can also be
   represented as a function of frequency (frequency domain
   representation). Normally, the fast Fourier
   transform (FFT) algorithm is applied to compute the transformation
   from the time to the frequency domain. The plot showing the
   distribution of the power of the signal in the frequency domain
   is known as the power spectrum.

\end{description}

Acoustic emission is normally classified into two types of
signals: continuous and burst (transient) type. Continuous signals
arrive at the transducer in large numbers and thus distinct events
can not be detected, whereas burst type signals are characterized
by distinct events (Figure~\ref{fig:ae_types}).
\begin{figure}[h]
   \center{\includegraphics{ae_types.eps}}
   \caption{Continuous and burst type acoustic emission.}
   \label{fig:ae_types}
\end{figure}
In turning, continuous type AE is normally attributed to
deformation in the primary and secondary zones, and friction in
the secondary and tertiary zones~\cite{lan86}, while burst type AE
is attributed to chip and tool fracture \cite{dornfeld83}.
Additionally, chip impact and tangling also generate burst type AE
signals \cite{inasaki98}.

The major advantage of AE over other signals is that its frequency
range is much higher than that of the machine vibrations and other
environmental noises. Moreover, it is expected that AE is very
sensitive to metal cutting process characteristics since it is
generated by micro-mechanical activities in the cutting zone.


[Propagation of AE though the primary shear plane and tool-chip
interface \cite{uehara84}.] [pencil lead breaking test]
