Machining and Monitoring Strategies Part 11 ppsx

precise machine tool (Fig. 257a), and hoping to utilise
it to machine when approaching nano-metric resolu-
tion levels. ere are many oen interrelated factors
that have to be considered and then dealt with, if one
is to successfully operate at this ultra-precision level of
machining operations.
9.11 Machine Tool
Monitoring Techniques
Introduction
One of the most fundamental requirements for increas-
ing productivity of CNC machine tools, is the ability
to operate them ideally, in an untended manner, but
at the very least, minimally-manned – whether they
are ‘stand-alone’ machines, or part of a exible manu-
facturing cell, or system (FMC/S). So, if an untended
operation has been decided upon, then the absence
of an operator will create a considerable number of
problems that must be overcome, if the machining op-
erations are to be satisfactory. ese problems arise in
performing the monitoring and service functions that
are usually seen by the operator, who would normally
undertake the: monitoring of the cutting tool’s condi-
tion and its performance; replacing worn, or defective
tooling by interrupting the cutting cycle; assessing the
workpiece quality during machining; changing speeds
and feeds – if required; plus responding to unusual
conditions that are either seen, or heard, during the
cutting operation. While, in an unmanned machin-
ing environment, the associated monitoring systems
must provide the ‘articial intelligence’ (AI), necessary
to ‘mirror’ the experience gained by a fully-skilled op-

erator and their instinctive reactions and, to provide
the type of expertise usually associated with human
involvement. To cope with these every-day human-
intelligence activities and their subsequent interven-
tion during any machining operations, a considerable
number of monitoring systems have been developed.
In general, monitoring systems can be classied as:
process-monitoring; workpiece-monitoring; machine
tool monitoring; and tool-monitoring systems. Typical
applications of these monitoring systems for untended
operation on machine tools, include:
•
Monitoring the correct loading of the workpiece,
correcting any set-up misalignments, or datum o-
sets, while checking the quality of the workpiece,
•
Checking that the correct tools are available, by
identifying both the tools and their setting osets,
monitoring for tool wear and breakage and, initiat-
ing tool replacements – as necessary,
•
Adjusting speed and feed as appropriate and, com-
pensating for such eects as tool wear, thermal de-
formation and chip congestion,
•
Monitoring of machine elements, including the
CNC controller and taking any necessary correc-
tive action in response to: program failure; diag-
nostic error messages; etc.
Whatever the function that is to be monitored, there

is a need for some form of sensor to be incorporated
into the system – to detect any problems as they arise,
so that action can be appropriately taken, if necessary.
us, a sensor’s output – triggered by an error mes-
sage, must be processed to obtain the correct informa-
tion, allowing decision(s ) to be made. e machine’s
control unit, will then receive this ‘sensed’ result and
initiate controlled actions to either correct, or re-
cover the situation. Various types of monitoring and
sensing systems are currently available for machine
tools. Although, because this subject matter is so vast
and sophisticated, only several of these monitoring
techniques and sensing systems will now be consid-
ered.
.. Cutting Tool Condition
Monitoring
Introduction
Whenever an operator is present during a machining
operation, one of their major functions is to monitor
the tool’s condition while the cutting continues, where
they continually assure themselves that a tool in-cut
is performing productively. e tool-related monitor-
ing functions performed by an operator during any
component’s manufacture, may be classied into four
groups, briey these are:
1. Tool identication – this ensures that the correct
tool will be used for a specic operation, with a va-
riety of techniques being employed to achieve this
crucial tooling activity. Techniques include the use
of: touch-trigger probes (Fig. 133); non-contacting

probing methods (Fig. 134); ‘tagged’ tooling of the
contact (Figs. 116 and 117), or non-contact variet-
ies (not depicted),
Machining and Monitoring Strategies 
2. Tool-oset measurement – of the cutting edge’s po-
sition is necessary, in relation to that of the part’s
datum point. is can be accomplished by the
‘probing-techniques’ and tool identication meth-
ods mentioned above,
3.
Tool life monitoring – is necessary to estimate the
extent of a worn tool’s condition, which must be
replaced prior to tool failure. e are a range of
sensing devices available and they can be classied
into two main groups: ‘Direct sensing’ – include:
radioactive techniques; measurement of electrical
resistance; optical observation of the wear zone;
measurement of workpiece dimensional changes;
or the distance between the workpiece and the
tool post, ‘Indirect sensing’ – based upon either:
temperature; sound; vibration; acoustic emission
and force. is latter method can be measured
and monitored either directly, by dynamometry
(Figs. 178–180, 237 and 244), or indirectly via mea-
surements of power, current, or torque – some of
these techniques will shortly be discussed,
4.
Tool breakage detection – can be monitored to en-
sure that the cutting edge does not fail in-cut, as
damage to both the tooling assembly and the work-

piece may occur as a result. Once again, a variety
of commercially-available techniques based upon
force-related signals are available, including: those
methods that use a dynamometer, either situated
on the tool block, or in say, a turning operation be-
low the tooling turret (Fig. 179); thrust-/feed-force
sensors (Fig. 258); spindle-bearing/motor-current
monitoring (not shown); power-/torque monitor-
ing (Fig. 259a). is latter technique (Fig. 259a),
is oen known as: ‘Torque-controlled machining’
(TCM).
NB In order to fully appreciate the complexity and
sophistication of any tool- condition monitoring,
on CNC machine tools, the following section has
been included.
Tool-Condition Monitoring –
With Feed-Force Sensors
Modern microprocessor-designed tool-monitoring
systems can be utilised for a variety of reasons, for
example, to monitor the tool’s condition, or to reduce
machining value-added costs. e advantages of using
monitoring detection, are
•
Tool wear is monitored and tool changes initiated
when necessary, so avoiding damage to the ma-
chine, or workpiece,
•
If breakage occurs, a signal will be immediately
produced to stop the machine tool – usually within
milliseconds,

•
e system detects if a tool, or workpiece is miss-
ing, thus eliminating wasted machine time and the
likelihood of unpredictable crashes.
While the cost advantages of using tool monitoring
are:
•
Tool life can be optimised, meaning that the tools
need to be only changed when they are worn – to a
specied amount (Figs. 174 and 176) and so reduce
the tool costs (Fig. 177e),
•
Down-time (i.e. here, it is normally associated with
unanticipated wear rates, or tool crashes) is re-
duced, which increases the machine’s output and as
a result, improving cycle-time and costs per part,
•
Repairs to the machine tool and cutting tools may
be reduced to a minimum, so the maintenance costs
are lower,
•
e machining operation is automatically moni-
tored, limiting any costly labour rates by subse-
quent operator involvement.
e above listed advantages for tool-condition moni-
toring are quite an impressive recommendation, but
how does it achieve consistent and accurate tool moni-
toring, while simultaneously controlling the cutting
process? ese questions will now be considered, deal-
ing in the rst instance, with how the system monitors

the tool’s performance during machining.
A well known fact is that a tool will produce rela-
tively high loads during a cutting operation, as it
begins to wear. For eective ‘process monitoring’ it
is important that the signal utilised should vary in a
progressive manner as the tool wears and, not just at
the time that it actually breaks. It has been shown (Fig.
258b) that during a machining operation, the axial
force component (F
A
) provides a better indication of
the cutting edge’s condition as a function of tool wear,
than the torque value (M). us, the increase in the
axial force is more clearly dened – in both cases, from
that of a sharp tool (Fig. 258b – le) to that of a worn
tool (Fig. 258b – right). is change in the force gener
-
ated whilst cutting is instantly detected by a feed force
sensor (Fig. 258a). e sensor transforms the force
change into an electrical signal which is transmitted
 Chapter 
Figure 258. Tool-condition monitoring on a turning centre. [Courtesy of Sandvik Coromant].
Machining and Monitoring Strategies 
to the signal-processing device. Once the signal is re-
ceived, the processing device can immediately initiate
action by the machine’s CNC controller, if the tool is
either: worn, broken, or not in-cut. is situation is
all very well, but when should tool monitoring take
place and, what action should result? In Fig. 258a (i.e.
the inset diagram), the graphical depiction shows how

continuous monitoring of the axial force can be used
to triggered several alarm-states:
•
Level I – can be utilised to monitor whether a tool is
in-cut, or not-in-cut, as the situation arises, mean-
ing that either the tool, or component, or indeed
both, are missing,
•
Level II – can be used to detect tool wear, with the
alarm signal being used to initiate a tool change (i.e.
to a ‘sister tool’) on completion of the operation,
•
Level III – can be utilised for tool breakage, with
the signal being used to immediately stop the ‘feed-
ing-function’ of the machine tool, when breakage is
detected,
•
Tool crashes – a further level can also be employed
for crash protection, which acts in a similar man-
ner to ‘Level III’ , but this alarm immediately stops
all motions and in so doing, protects the machine
tool
76
.
In Fig. 258c, the schematic diagram illustrates typi-
cal monitoring positions on a two-axis turning centre,
showing potential sites to place the sensors, such as on
the ball-screw nuts of the recirculating ballscrew as-
semblies, for both the X- and Z-axes. Not shown here,
but normally also tted is a current sensor. us, these

signals are continuously monitored by either a single-
or multi-channel control unit, as will be the control
signals from the machine tool’s CNC controller. Any
alarm signals triggered, being passed back through a
closed-loop to the machine’s control unit for appropri-
ate action to be taken, or indeed if any. e function of
a typical commercially-available multi-channel signal
processing unit, might be to:
•
Sense – then process tool-cutting information from
signals at the various sensors and sites for the mul-
tiple channels of the unit,
76 ‘Tool breakage detection times’ , it is possible to vary the reac-
tion time, which is usually between: 0.1 to 1 second, but for
any form of tool breakage a shorter reaction time is desirable,
typically ranging from: 1 to 10 milliseconds.
•
Learn – by automatically memorising the signal
values obtained from the sharp cutting tools, whilst
in this ‘learn-mode’
77
,
•
Stores data – for a signicant number of cutting
operations per channel in its memory for each cut-
ting operation, as well as automatically setting the
appropriate levels for each alarm signal from its
memory,
•
Reacts – by sending alarms to the machine’s control

unit, informing it if the tool is either: worn; broken;
or not-in-cut,
•
Coordinates – automatically, machining and moni-
toring on commands from the machine’s control
unit,
•
Adapts – to the particular machine and its cutting
environment: once installed and programmed to
suit the machine tool, with the setup parameters
being modied to adapt to any further machining
requirements,
•
Communicates – between the operator and the
machine via the control panel, informing the oper-
ating personnel about cutting tool conditions and
providing an interface for control of all functions.
In Fig. 258c, this line-diagram depicts a typical turn-
ing centre application of tool condition monitoring.
e machine is controlled on two axes, with sensors
on the feed-drive bearings of both the X- and Z-axes.
A representative nominal force for these sensors is 40
kN, but this rating will depend upon the end-user’s re-
quirements. e sensors can be designed for tapered,
or angular contact bearings, or for a combined axial
and radial bearing application – suiting the particular
machine tool. When tool monitoring is needed for a
four-axis turning centre, two tool monitoring units are
usually needed, since each turret (i.e. to and bottom)
can be operated both independently. e key elements

in any tool condition monitoring situation are the
sensor’s position and its design. For universal instal-
lation on a variety of machine tool congurations, the
positioning of the sensing devices is usually on the re-
circulating ballscrew nut assembly.
77 ‘System-learning’ , in the past this was somewhat a basic of
functional performance, but with the advent of ‘articial neural
networks’ , they have an ‘AI-ability’* to ‘mimic’ human involve-
ment and react to their environment – once ‘trained’. More
will be said on this ‘AI-topic’ in the nal section of this book.
* ‘AI’ is a term that is normally utilised when some form of ‘arti-
cial Intelligence’ is employed in the decision-making process.
 Chapter 
Much more could be said concerning the informa-
tion on their: operational setup; range; and adaptabil-
ity; for these tool condition monitoring systems. In
the interests of brevity, the reader should look to the
manufacturers of such equipment, or the references
and available literature for more specic in-depth in-
formation.
.. Adaptive Control and Machine
Tool Optimisation
Adaptive Control
Adaptive control systems have been utilised since their
introduction in the 1960’s, where their operational
performance and reliability was somewhat dubious,
because of the type of sensors utilised, the speed of
signal-data processing and their installation on the
machine tool. Many of these early systems attempted
to undertake many functions simultaneously and were

oen termed; ‘adaptive control optimisation’ (ACO),
but due to the problems mentioned above, they were
somewhat unreliable and as such, fell out of favour.
Later, a more pragmatic approach to adaptive control
constraint (ACC) was introduced called: ‘torque-con-
trolled machining’ (TCM), which oered a simpler
termed: ‘feed-only system’ – with a typical system be-
ing depicted in Fig. 259a. us, the operation of a TCM
system, involves unique sensory circuitry and compu-
tation methods that measure the net cutting torque,
then compares this value obtained, to that of the preset
torque limits – these previously being established for
the cutting tool and workpiece combination utilised.
e appropriate control actions, namely, a feedrate
reduction is then automatically taken, whilst keeping
within the maximum torque and power limits of the
spindle motor. If a condition arises where the feedrate
falls below a preset limit, a new tool (i.e. sister tool) is
called-up to complete the machining operation. is
feedback-loop in which continuous monitoring by the
sensors and updating the machine control unit – using
adaptive control, produces optimal cutting conditions
for the tool and workpiece combination.
Adaptive control via TCM (Fig. 259a), basically op-
erates in the following manner. Prior to its activation
and if for example, a variation of stock was present for
roughing operation with a large face-mill. e unpre-
dictability of the height of this stock if a TCM system
was not activated, might otherwise over-load the cut-
ting edges, possibly causing damage to the: cutter as-

sembly; workpiece; or even the machine tool. Once
the TCM has been correctly activated and preset to a
torque limit, then if the D
OC
is large, the control sys-
tem senses a torque increase and simultaneously the
feedrate over-ride is initiated. is over-riding of the
programmed feedrate decreases the feed for this large
D
OC
, it will then increase as the D
OC
lessens, or rapidly
move over an ‘air-cut’ , thus producing optimal cutting
tool protection and eciency as the chip-load is more
uniform, regardless of the variable D
OC
’s. Even if there
is no discernible dierence in the relative height of the
D
OC
taken, but the bulk hardness of the part may vary
by up to 300% in some cases, machining with the TCM
activated will protect the tooling. So to mention the
some benets to be gained from TCM, they include:
extended tool life; optimised feedrates – without the
risk of tool damage; higher throughput of machined
parts; tool breakage minimised; quicker setup times;
and reduced operator intervention. Obviously very
small diameter tooling, may not respond to the torque

demands so readily, but for most machining opera-
tions and tool/workpiece combinations the system has
distinct benets to the overall machining production
process.
To summarise the principal benets of utilising
some form of adaptive control system, they are:
•
Main spindle motor is protected from overload,
•
Damage to the cutter and to the expensive value-
added workpiece are protected,
•
Optimal stock removal rates are possible, under
steady-state machining conditions,
•
Using a constant: cutting power; cutting force; and
feed force; optimises tool life,
•
If unpredictable air-gaps occur – whilst cutting, the
fastest tool travel is utilised,
•
Where workpiece hardness signicantly varies, tool
edges are protected by adjustments of the chip-
loads,
•
Where an operator’s experience, or the program’s
eciency may dier for varying cutting operations,
the adaptive control system eliminates this ‘techni-
cal gap’ ,
•

ere is no over-shooting of the permitted cutting
power during re-entry into the workpiece material
whilst machining the part under regular condi-
tions.
Costs vary the for ‘post-installation’ of adaptive con-
trol systems to CNC machine tools, but at today’s
prices they range from: $ 9,000 to $ 15,000 (US). How
-
ever, once installed they last the life of the machine
Machining and Monitoring Strategies 
Figure 259. Either use: adaptive control or CNC program optimisation – for variable tool path trajectories.
 Chapter 
tool, giving a superb pay-back on the original invest-
ment, when one considers the major benets listed
above.
Machine Tool Optimisation
If a company has signicant numbers of CNC machine
tools in their manufacturing facility, then it may not
be feasible to introduce an ‘adaptive control’ system
across all of these machines – despite the positive
merits described above, simply on nancial grounds
alone. Under such circumstances, perhaps a ‘soware-
approach’ by simulating the cutting operations to the
problem of machining optimisation, may be the way
forward? Some companies oer CNC programming
optimisation packages that are based upon literally
thousands of ‘man-hours’ of development and rene-
ment (i.e. Fig. 259b, shows a very sophisticated version
of such a tool verication and simulation system).
ese simulation systems are oen part of a larger: op-

timisation; verication and analysis product that can
be ‘tailored’ to suit a machining company’s product
range and manufacturing output. ese ‘knowledge-
based’ systems of the machining process, via previous
simulation, know the exact: D
OC
; width of cut; and
angle of cut (i.e. for cutter orientation, when prol-
ing); for the machining process under consideration.
Further, the system also knows how much material
is to be removed by each cutting edge, as such, the
system also has information on the tooling available
from the magazine, therefore it selects correct tool and
assigns to it the optimum feedrate. Moreover, once
this information has been established for the new tool,
it outputs the tool path – which was identical to say,
that of the original tool, but now having signicantly
improved feedrates, although the system does not alter
its trajectory.
While setting up the system, it is usual for such
soware (Fig. 259b) to prompt the user for cutter set-
tings as the part simulation occurs, by in essence, add-
ing the user’s intelligence to that of the cutter’s opera-
tion. With these systems it is usual to have all cutter
settings stored in an optimisation library, thus the user
only has to dene the setting once. While, the more
sophisticated systems nd the maximum volume re-
moval rate and chip thickness for each tool, then it
employs them to determine the optimisation settings
for that tool.

In optimised roughing-out, the objective here is ob-
viously to remove as much stock material as possible
in the fastest time. Conversely, for nish-machining,
chip-loads may vary considerably, as the tool proles
through the workpiece material that was le behind
during previous roughing cuts over the contours
– to near-net shape. By optimising the tool’s path, the
soware adjusts the feedrates to maintain a constant
chip-load
78
(Fig. 259b). is cutter optimisation will
improve the tool life and give an enhanced machined
surface nish to the component. is fact is especially
critical when ‘tip-cutting’ , with either a ball-nosed end
mill (Fig. 247b), or contouring over a surface with a
small step-over, such as when semi-nishing, or n-
ishing a steel mould cavity (Fig. 249b).
Summarising the advantages of utilising a simu-
lated optimisation cutter-path soware package, such
as the one in Fig. 259b which only illustrates some ba-
sic and simple tool paths. us, cutter-path optimisa-
tion oers the user the ability to:
•
Machine more eciently – cutting more parts in
the same amount of time, by signicantly reducing
the machined component’s cycle-time,
•
Reducing part cost thereby saving money – increas-
ing productivity by reducing the time it takes to cut
parts, will become a signicant saving per annum,

•
Improving part quality – by minimising the con-
stant cutting pressure, thus reducing cutter deec-
tion, with nished corners, edges and blend areas,
needing less subsequent hand-nishing,
•
Cutter life improved – because of optimised cut-
ting conditions are used, which prolongs tool life.
Moreover, with shorter in-cut time, this results in
less tool wear, also having the benet of reducing
down-time to change inserts, or tooling,
•
Reduction in machine tool wear – as a more con-
stant cutting pressure between the machine tool
and the workpiece reduces variable forces on the
axis motors, giving smoother machine operation,
•
Utilises time available more eectively – allowing
machinists to operate several CNC machine tools,
or setup the following job, etc., as they do not have
to be constantly ready to reduce/increase the ma-
chine’s feedrate over-ride.
By investing in suitable simulation and optimisation
soware of the tool’s path, enables a company that is
currently involved in a considerable amount of ma-
78 ‘Constant chip-loads’ , are normally recommended by cutting
tool manufacturers, as they reduce the eect of ‘chip-thinning’
somewhat.
Machining and Monitoring Strategies 
chining activities to become very cost-eective and

ecient when compared to their direct competition,
both nationally and internationally. One could cer-
tainly ask the question, under these circumstances
just mentioned: ‘Can a company aord not to be using
such soware, if their main competition – both here and
abroad have it available now?’
.. Artificial Intelligence:
AI and Neural Network
Integration
Introduction
Over the past decade and a half, some signicant ad-
vances in machining materials have occurred, while
complementary progress has also been made in the
machine tool’s CNC controllers, coupled to their faster
micro-processor speed and additional technological
renements. Many of these machine tools are inte-
grated into fully-automated systems machining lines
– for volume part production purposes, or into ex-
ible manufacturing cells/systems (FMC/S) – allowing
scope for mixing batch sizes and perhaps employing
a ‘Group Technology’ (GT) approach (i.e. see Footnote
24, Chapter 6). So that the full potential of these ma-
chine tools can be exploited, it is exceedingly impor-
tant that production processes are both monitored and
controlled in an ‘intelligent manner’.
Previously, when little cutting data and minimal
tooling-related behaviour had been established for a
new production run, it was necessary to instigate some
form of tool measurement procedure. So, aer operat-
ing a cutting tool for an extended time-period in-cut,

so that the tool’s wear pattern (Fig. 174) had begun to
reach the end of its productive life (Fig. 176), it was
necessary to exchange it for a new tool. is arbitrary
tool-changing strategy was at the discretion of the op-
erator, therefore it relied upon their past machining
experience to decide when it was advisable to instigate
the necessary down-time – for this tooling-related ac-
tivity. An alternative approach, was to employ some
form of condition monitoring procedure, by utilising
o-line direct measurements to ascertain the amount
of wear that had occurred so far. is assessment ac-
tivity entails a certain degree of operator competence
in a variety of disciplines, because the cutting tool’s
inspection required microscopical analysis by metro-
logical/metallographical techniques to determine the
current status of the tool’s cutting edge(s). is tool-
ing investigation necessitated that the tool be at rest
and out-of-cut, so that its life could be correctly estab-
lished, which can be a costly and time-consuming pro-
cess, diminishing the cost-eectiveness of the overall
production process.
One machining strategy that can be used to over-
come most production deciencies, is to have some
form of on-line, indirect system, which has the ad-
vantages of being benecial in terms of: improved
running costs; enhanced component quality; and e-
ciency in production performance. In order to achieve
such benecial tooling-related and part production
enhancements, it is necessary to utilise some form of
‘on-line tool condition monitoring’. So that this tool

monitoring objective can be successful, a number of
hard- and so-ware activities must be undertaken,
then integrated into a usable ‘workshop-hardened’
instrumental package. In the early-to-mid 1990’s a
novel approach to this problem, but also included the
some distinct renements by: ‘on-line tool condition
monitoring – using neural networks’ was developed by
Littlefair et al. (1995). is fundamental and applied
research work was fully-supported by a range of in-
dustrial companies, it was later also installed at sev-
eral widely-diering manufacturing companies. In or-
der to comprehend the complexity of such an on-line
tooling related activities, the following case-study has
been included (Littlefair, et al., 1995), as it succinctly
describes the hard- and so-ware issues that had to be
overcome.
.. Tool Monitoring Techniques –
a ‘Case-Study’
e technique of tool wear monitoring can be classi-
ed in two distinct manners, these are by either:
•
Direct monitoring – produce accurate results, but
they are dicult to fully-implement in a shop-oor
environment,
•
Indirect monitoring – considers various parameters
which change as a result of increasing tool wear.
e latter tool monitoring strategy was utilised in a
single-point turning operation on a CNC turning cen-
tre, by incorporating: tool force; vibration; and acous-

tic emission; by being integrated into a neural network;
and this theme will now be mentioned. Each of these
monitoring systems will be briey described, plus the
neural network – appropriate for complete sensor-fu-
sion, will then be described.
 Chapter 
Tool – Force Monitoring
In single-point turning, if one ignores the orthogonal
cutting condition, then for oblique cutting three re-
actionary forces are experienced by the tool, termed:
tangential; axial; and radial force components (Fig.
19a). e tangential force is generated due to the
workpiece’s rotation, this being by far the greatest of
the three forces. An axial force component is the re-
sult of the applied feed force, while the radial force is
a function of, in the main, the inclination of the ap-
proach angle and to a lesser extent inuenced by that
of the tool nose radius – this radial component being
the smallest of the forces. Each of these component
forces in oblique cutting are inuenced by a range of
factors, such as: workpiece material and its condition;
D
OC
; tool cutting insert geometry; and cutting data
utilised – speed and feed. In this case, a special-pur-
pose holder for a platform-based dynamometer was
manufactured (Fig. 261a).
Tool – Vibration Monitoring
In machining processes, the onset and subsequent
development of vibration orginates from the overall

dynamic behaviour of the tool-workpiece-machine
system. e anticipated vibrational causes can be both
cyclic in nature – resulting from changes due to com-
pression and sliding of the workpiece material in the
shear zone, and, changes in the frictional conditions in
the contact zones – between the tool and workpiece.
So that vibrational inuences during continuous cut-
ting could be monitored, accelerometers tend to be
utilised. Normally, accelerometers are situated as close
to the cutting edge as possible, usually at a convenient
position on the toolholder. e vibration parameters
monitored are usually related to either the toolholder’s
natural frequency, or the frequency of chip segmenta-
tion. Moreover, it is also possible to eectively utilise
that of a dynamometer’s ‘force signal’ for indirect vi-
bration monitoring.
Tool – Acoustic Emission Monitoring
Acoustic emissions (AE) are those high-frequency
stress waves generated due to the spontaneous energy
release in materials undergoing: deformation; fracture;
phase transformations; etc. us, AE signatures can be
divided into two distinct types: continuous – contain-
ing low-amplitude and high-frequency signals (i.e. in
the range: 100 to 400 kHz); burst – containing higher
amplitude and lower frequency signals (i.e. in the
range: 100 to 150 kHz). By the application of Fourier
transforms coupled to that of statistical analysis-based
techniques, it is possible to utilise both of them for the
analysis of AE signals. e root-mean-square (rms)
value has been shown to produce an increasing trend

with increased amounts of tool ank wear, further,
the combination of both skew and kurtosis of the AE
signal will also indicate a correlation with ank wear
rates.
Tool – Sensor Fusion and Multi-sensor Integration
e application of multiple sensors can be eectively-
employed in a complex tool-wear monitoring system
for machining environments, to obtain harmonizing
information about the turning production process.
is multi-sensor monitoring acts to rearm the ‘con-
dence factor’ , when dealing with the prospective di
-
agnostics from the single-point turning process. How-
ever, the exercise of utilising multiple sensors, entails
integration and fusion of the sensory information, to
extract the essential features from the data, by remov-
ing the ‘redundancy’ present in this data. In this re-
gard, the application of articial neural networks, can
provide the solution to the sensor-fusion and auto-
matic decision-making processes for this tool-condi-
tion monitoring system.
Artificial Neural Networks (ANN)
Articial neural networks (Fig. 260a), are composed of
many simple processing nodes which operate simulta-
neously. ese ANN’s mimic the functional behaviour
of biological neural network systems, allowing them
to be utilised to integrate and fuse information from
multiple-sensor sources. e functional behaviour
of the overall system is primarily determined by the
pattern of connectivity of the nodes (Fig. 260a). As a

system, ANN’s are capable of performing some high-
level functions, such as: adaptation; generalisation and
target-learning. ese capabilities are particularly rel-
evant for any form of tool-wear monitoring applica-
tions. e advantages of employing ANN’s to integrate
and fuse data, are their inherent capabilities to: adapt
to instructed environments; robustness to noise; fault
tolerance; simultaneous processing; and feasibility of
on-line realisation (i.e. via hardware implementation).
Possibly the most widely used ANN and the one
reported in this section, is that of the ‘multi-layer per-
ceptron’ type, which uses an ‘error-back-propagation
Machining and Monitoring Strategies 
Figure 260. Neural network architecture and tool condition monitoring system. [Source: Littlefair, Javed & Smith, 1995].
 Chapter 