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\chapter{Introduction} \label{chap:introd}


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\section{Metal Cutting Basics} \label{sec:cut_basics}

Metal cutting or machining may be defined as a process by which a thin layer of
metal, the chip or swarf, is removed by a wedge-shaped tool from a large body.
Operations that do not involve chip formation can also be called metal cutting, but
these will not be considered here. In the process of cutting, the metal removed is
deformed plastically, and a large amount of energy is necessary to form the chip and
to move it across the tool face. Hence, very high stresses are imposed and much heat
is generated.
\begin{figure}[h]
   \center{\includegraphics{turning.eps}}
   \caption[Turning operation.]
   {Turning operation. (a)~Engine lathe. (b)~Top view detail of the
   cutting zone.}
   \label{fig:turning}
\end{figure}
The importance of metal cutting may be appreciated by the observation that nearly
every device in our society has one or more machined surface or holes. The three most
widely used cutting operations are: turning, milling, and drilling. However, since
the present thesis deals uniquely with the process of turning, from this point on,
only this process will receive relevant attention; also, when one refers exclusively
to metal cutting in the context of a specific cutting operation, one is referring to
turning.

Turning, the simplest of all cutting operations, is a process in which the work
material is held in the chuck of a lathe and rotated. The tool is held rigidly in a
tool post and moved along the axis of the workpiece, cutting away a layer of metal to
form a cylinder or another profile (Figure~\ref{fig:turning}). The variables that can
be adjusted by the operator are: the cutting speed ($V$), the feed rate ($t_1$), and
the width of cut ($w$). The cutting speed is the rate at which the rotating uncut
surface of the work passes the tool; the feed rate is the distance moved by the tool
along the axial axis of the workpiece during one revolution; the width of cut is the
thickness of metal removed from the workpiece in the radial direction.

\begin{figure}[h]
   \center{\includegraphics{tool.eps}}
   \caption{Turning tool.}
   \label{fig:tool}
\end{figure}
\begin{figure}[h]
   \center{\includegraphics{insert.eps}}
   \caption{Turning tool holder and insert.}
   \label{fig:insert}
\end{figure}
Figure~\ref{fig:tool} shows the main features of a cutting tool used in turning
operations. The surface of the tool over which the chip flows is called the rake face;
the cutting edge is formed by the intersection of the rake face with the clearance face
of the tool; the clearance angle must be adequate so as to avoid contact between the
tool and the newly cut metal surface; the rake angle ($\alpha$) is measured on a plane
perpendicular to the cutting edge; the tool terminates in the end clearance face; the
nose of the tool is the intersection of all these three faces. The major classes of
tool materials include (in order of increasing hot hardness but in order of decreasing
toughness)~\cite{shaw84}: carbon steels, high-speed steels, cast alloys, tungsten
carbides, cermets, titanium carbides, ceramic, polycrystalline diamond and cubic baron
nitrits, and single crystal diamond. With the introduction of carbide materials, it
became uneconomic to make the whole tool of one material. Consequently, small tool
inserts started to be produced in order to be clamped on a tool holder
(Figure~\ref{fig:insert}). Indexable inserts are presently manufactured in a variety of
shapes, having a number of cutting edges, so that when a cutting edge is worn out, the
insert is unclamped and rotated to an unused cutting edge. When all edges are worn out,
the insert is discarded rather than reconditioned.

Cutting can be simplified to a process called orthogonal cutting. In this process,
the tool has a single and straight cutting edge, which is set normal to the cutting
speed, removing a layer of work material of uniform thickness~$t_1$ and width~$w$
(Figure~\ref{fig:orthogonal_cutting}).
\begin{figure}[h]
   \center{\includegraphics{orthogonal_cutting.eps}}
   \caption{Orthogonal machining process.}
   \label{fig:orthogonal_cutting}
\end{figure}
In turning, these conditions are secured by cutting only with the straight edge of
the tool, which must be set normal to the cutting speed and feed rate directions, and
by employing a workpiece in a tubular form whose wall thickness corresponds to the
width of cut. In this method, the cutting speed is not exactly constant along the
cutting edge, being slightly greater at the outside of the tube. However, if the tube
diameter is reasonably large, this difference between speeds can be neglected. If
turning is performed with a solid workpiece, the process is called semi-orthogonal,
for the conditions at the nose of the tool are different from those at the outer
surface of the bar. To avoid a great discrepancy from orthogonal conditions, the tool
nose must be small relatively to the width of cut, so that most of the cutting edge
engaged in cutting is straight. Orthogonal cutting may be simplified to a
two-dimensional system as shown in Figure~\ref{fig:cutting}. The uncut chip
thickness~$t_1$ corresponds to the feed rate associated with turning operations, so
that uncut chip thickness and feed rate can be treated indistinctively. The chip
flows over the tool rake face with thickness~$t_2$ and speed~$V_\mr{C}$.
\begin{figure}[h]
   \center{\includegraphics{cutting.eps}}
   \caption{Two-dimensional representation of a cutting process.}
   \label{fig:cutting}
\end{figure}


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\section{Metal Cutting Monitoring}

The objective of any machining process is the production of a part of a specific
shape with adequate dimensional accuracy and surface condition from an appropriate
workpiece. However, some cutting process abnormalities, such as tool wear, tool
breakage, machine vibration (chatter), and improper chip formation may affect the
quality of the finished machined part. Moreover, effective control of the condition
of the tool may reduce tooling costs and down time. Subsequently, cutting processes
must be monitored, which has been traditionally done by human operators. The
introduction of automated machining systems have greatly increased productivity and
decreased human involvement. However, for the successful implementation of fully
unmanned machining systems, monitoring has to be performed with sensors and
associated decision-making systems, which are able to interpret the incoming sensor
information and decide on the appropriate corrective action.

The signals employed in the monitoring of a cutting process can be classified into
two categories: direct and indirect. In direct signal monitoring, the actual variable
of interest is directly measured, and, despite its high accuracy, it is rarely
utilized because of the high cost and difficulty of installation of the sensors. In
contrast, indirect signal monitoring utilizes sensors that are relatively economical
and small, and they can be used for on-line monitoring if a relationship between the
sensor signal and the variable of interest can be established. The most common
indirect signals include: cutting forces, cutting power, vibration, sound,
temperature, surface roughness, and acoustic emission (AE).

An advantage of AE from the other indirect sensing techniques is that AE is
considered to be directly related to the micro-mechanical activities of the cutting
process. Also, since AE is normally a high frequency signal, unwanted lower frequency
noise signals from the machining process can be filtered off from the signal of
interest. As a result, AE has been used in various monitoring applications.
Reuben~\cite{reuben98} gives a brief overview, not only on the use of AE in metal
cutting monitoring, but also on other industrial applications to which AE monitoring
has been successfully implemented. The use of AE to monitor the state of a cutting
process has been investigated by several authors, and some promising results have
been obtained. There are two main areas of interest in metal cutting monitoring:
\begin{description}

   \item[Tool condition] The use of AE has been employed in the detection of both
   progressive tool
   wear~\cite{inasaki81,lan82,messaritis86,naerheim88,blum90,dimla00,li02}
   and also sudden tool failure due to cracking, chipping, and
   fracture~\cite{moriwaki80,lan84,diei87,haili03}.

   \item[Chip management] Another application of AE is the detection of the different types
   of chip formation during metal cutting and also the detection of undesirable
   congestion or entangling of chips~\cite{dornfeld83,uehara84,dornfeld85}.

\end{description}
However, despite much effort has been directed towards developing on-line condition
monitoring systems that make use of features extracted from the AE signal, few of
them have shown to be reliable enough as to be implemented in
industry~\cite{iturrospe05}.

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\section{Thesis Objectives and Structure} \label{sec:obj_struct}

In order to improve the accuracy of the AE monitoring techniques, the mechanisms by
which AE is generated must be understood. As shown by two quite recent review
papers~\cite{dimla00,li02} and from the introduction of a very recent
publication~\cite{iturrospe05}, most studies on the generation mechanisms of AE due
to metal cutting have been accomplished during the 1980s and beginning of the 1990s.
Since then, most research effort has been put on the study of a number of, more or
less, sophisticated techniques that make use of the AE signal for monitoring metal
cutting operations~\cite{dimla00,li02}. From the studies on the origin of AE during
metal cutting, some models have emerged; however, none of these models has given a
completely satisfactory solution for the problem, and although much insight has been
gained on the field, the exact mechanisms of AE from metal cutting are still not
fully understood.

As a consequence, it is the general objective of the present thesis to investigate
the generation mechanisms of AE during metal cutting, so that the generation
mechanisms of AE are understood, and a relationship between AE and metal cutting is
achieved. A comprehensive review on the most significant theories and techniques
achieved in the fields of metal cutting, AE, and the relationship between the two is
undertaken. Most suitable signal processing techniques are employed in the analysis
of the data from a comprehensive set of metal cutting experiments, where different
work materials and a wide range of cutting conditions are utilized. In addition, a
semi-empirical metal cutting theory is employed as a metal cutting model, so that a
relationship between plastic deformation and AE can be investigated. The main
techniques utilized in the processing and analysis of the experimental data and also
the most relevant theories utilized in the modelling of AE due to plastic deformation
accompanying metal cutting are summarized next:
\begin{itemize}

   \item A semi-empirical model developed by Oxley and co-workers~\cite{oxley89} is
   used, so that important parameters that define the physics of metal cutting, such
   as the geometry of the process, stresses, strains, strain rates, and temperatures
   can be estimated. With these parameters, a relationship between plastic
   deformation and AE is analysed and discussed.

   \item A signal processing technique, named AE mode, which removes the influence of
   erroneous and undesirable outlying values from the AE data~\cite{blum90}, is
   utilized to compute the energetic component of the AE signals.

   \item In order to compute a measure of the frequency content of AE, a technique,
   called mean frequency, which divides the AE spectrum into two parts of equal energy,
   presented by Rangwala and Dornfeld~\cite{rangwala91b}, is used.

   \item In addition to AE, force and temperature signals are measured during the
   experimental procedure. Since Oxley's model also predicts cutting forces and
   temperatures, these two quantities are used to evaluate the accuracy of the
   model. However, since the temperatures are measured remotely from the cutting zone,
   the finite-difference technique~\cite{holman02} is employed, so that the cutting
   temperatures can be estimated from the remote measurements.

   \item As dislocation motion is considered to be the main mechanism responsible for
   plastic deformation in metals~\cite{hull84,honeycombe84}, it is suggested, as a
   result, that some mechanism involved in the motion of dislocations should generate
   the detected AE waves. Since most theories that relate AE to plastic deformation
   apply only for processes that occur at much lower strain rates than those
   encountered in metal cutting~\cite{skalskyi03,ono05}, a theory presented by
   Kumar~\etal~\cite{kumar68,kumar69}, which takes into account plastic deformation
   at high strain rates and temperatures, is utilized, resulting in the development
   of a novel qualitative model, which is observed to be in agreement with both
   experimental results and Oxley's model predictions.

\end{itemize}
The development of the proposed work is going to be described throughout the
following chapters of this thesis, and this will be done according to the following
structure:
\begin{description}

   \item[Literature review (Chapter~\ref{chap:review})] The relevant knowledge
   necessary for the understanding of the two main fields investigated throughout
   this thesis, i.e.\ metal cutting and AE, is introduced. In the first part, metal
   cutting definitions and basic process mechanics are explained, followed by the
   most significant metal cutting models; two types of models are analysed: models
   with constant material properties and with variable material properties. Other
   areas, such as cutting temperatures and tool wear, are also briefly reviewed. The
   second part deals with the process of AE generation. To begin with, the concept of
   AE is defined; then, the processes by which AE is generated are examined, focusing
   specially on the process of dislocation movement accompanying plastic deformation;
   other stages of the AE process, such as propagation, detection, signal
   conditioning, and signal processing are also examined. Afterwards, metal cutting
   is evaluated as a source of AE, and different models of AE generation during the
   course of metal cutting are reviewed.

   \item[Experimental methodology (Chapter~\ref{chap:method})] In order to study the
   causes of the detected AE during metal cutting, a series of cutting experiments was
   carried out. The methodology adopted in the experiments, including the turning
   machine, the tooling system, the work materials (two carbon steels, a stainless
   steel, and an aluminium alloy), the sensors, and the testing conditions, is
   described here. The following signals were acquired: AE, cutting forces, vibration,
   and temperature. Two different types of cutting tests were accomplished: the first
   type utilized  a wide range of cutting conditions, and the second type utilized
   preheated workpieces before cutting.

   \item[Experimental results (Chapter~\ref{chap:results})] Experimental results are
   shown for the two experiments described previously. Quite simple data processing
   was done with the acquired raw data, for, at this stage, the objective was to
   provide a general overview on the variation of the AE level with the basic cutting
   parameters $V$, $t_1$, and $w$. Cutting forces and temperature data are also
   presented.

   \item[Analysis and discussion (Chapter~\ref{chap:discuss})] To begin with, a
   preliminary discussion on the experimental results previously presented is
   undertaken; published results are also reviewed in order to be compared with the
   present ones. An empirical model relating the AE level to $V$ and $t_1$ is
   presented next, so that the variation of AE with $V$ and $t_1$ can be easily
   visualized. A type of model, which has been widely used by many authors, that
   relates the power of AE with the power of cutting is discussed subsequently; it is
   concluded that these models are partly incorrect. Afterwards, a semi-empirical
   theory (Oxley's model) that simulates the cutting process is analysed; simulated
   force and temperature results are compared with the current experimental results,
   and reasonable agreement is found between experimental and simulated results.
   However, since the temperature sensors were placed some distance away from the
   cutting zone, the finite difference technique was utilized in order to compute the
   temperature at the cutting zone by employing the sensors data. In order to explain
   why the rate of increase of the AE level decreases with $w$, a very simple model
   that simulates AE generation and propagation is developed; it is concluded that
   increasing $w$ changes the phase delay of the AE waves arriving at a sensor placed
   on a remote location (AE events further apart result in higher phase difference of
   remote AE waves), whose effect is believed to be responsible for the lowering of
   the detected AE\@. Moreover, an analysis of the basic parameters involved in plastic
   deformation shows that the power of AE increases with primary strain and secondary
   strain rate, but it decreases with cutting temperature; besides, the frequency
   content of AE increases with strain rate and decreases with temperature. Finally,
   since dislocation motion is the basic process governing plastic deformation in
   metals, a qualitative model that predicts that AE is generated when dislocations
   surmount obstacles at the typical high strain rates and temperatures encountered
   in metal cutting is proposed; fairly good agreement is obtained by comparing model
   predictions with the current experimental results.

   \item[Conclusions and recommendations (Chapter~\ref{chap:conclus_recommen})]
   Finally, the conclusions encountered during the previous analysis are summarized
   in this chapter. Additionally, recommendations of further work that should be undertaken are
   also provided.

\end{description}
