THE ADAPTABILITY OF A TOOL WEAR MONITORING SYSTEM UNDER CHANGING CUTTING CONDITIONS
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Mechanical Systems and Signal Processing (2000) 14(2), 287}298
doi:10.1006/mssp.1999.1286, available online at http://www.idealibrary.com on
THE ADAPTABILITY OF A TOOL WEAR
MONITORING SYSTEM UNDER CHANGING
CUTTING CONDITIONS
R. G. SILVAR, K. J. BAKER AND S. J. WILCOX
School of Technology (DoDAT), University of Glamorgan, Pontypridd, Wales CF37 1DL, U.K.
AND
R. L. REUBEN
Department of Mechanical and Chemical Engineering, Heriot-Watt University,
Riccarton, Edinburgh, EH14 4AS, U.K.
(Received 6 April 1999, accepted 13 December 1999)
The process of metal cutting is a complex phenomenon that has been researched for many
years but the aim of practical cutting tool condition monitoring has yet to be achieved.
Previous work by the current authors using two neural networks (to classify acquired data)
moderated by an Expert System (based on Taylor's tool life equation) has shown that it is
possible to accurately monitor tool wear with a single machine/tool/material/cutting condi-
tion combination and to identify any inconsistencies between the predictions of the neural
networks and engineering practice. This paper investigates the e!ects that minor inconsist-
encies in cutting conditions might have on such a system by determining the &zone of
in#uence' of this working system by systematically varying the cutting conditions whilst
keeping all other variables "xed. The investigation has found that the zone of in#uence is
small but usable, and an approach to the utilisation of the system in a machine shop is
suggested.
2000 Academic Press
1. INTRODUCTION
Conventional machining systems rely heavily on human operators for monitoring the
process, taking the appropriate action in the event of a problem, inspecting the quality of
the product, controlling the process and material handling. However, in recent years,
manufacturing industry has been moving towards automated, un-manned machining to
improve productivity and reliability. Thus, the implementation of an intelligent machining
system, which can perform speci"ed machining operations without detailed input from
human operators in a harsh and unpredictable shop environment, has become increasingly
important. This work concerns an aspect of this general problem, namely the adaptability of
a tool wear monitoring system under relatively minor changes in cutting conditions. The
development of this system, which employs a combination of arti"cial neural networks
(ANNs) for sensor-based wear level detection with an Expert System (ES)-based surveil-
lance system, which moderated the sensor-derived diagnosis, is reported elsewhere [1].
R Now at Universidade Lusiada, Edi"cio da Lapa, Largo Tinoco de Sousa, 4460 Vila Nova de Famalicao,
Portugal.
0888}3270/00/030287#12 $35.00/0 2000 Academic Press288 R. G. SILVA ET AL.
Neural networks can perform arbitrary non-linear functional mappings between sets of
variables [2], a single neural network could, in principle, be used to map the sensor data
(raw input data) directly onto the required wear level ("nal output). In practice, for all but
simple problems, such an approach will generally give poor results due to the fact that real
data often su!ers from a number of de"ciencies such as missing input values or incorrect
target values. For most applications, it is necessary "rst to transform the data (de"ne
features) into some new representation (a de"ned set of features) before training the neural
network [3]. To some extent, the general-purpose capabilities of a neural network results in
less emphasis having to be placed on the careful optimisation of this preprocessing than
would be the case with other techniques. Nevertheless, in many practical applications of
ANNs, the choice of features will be one of the most signi"cant factors in determining the
performance of the "nal system [4]. In the simplest case, preprocessing may take the form of
a linear transformation of the input data, and possibly also of the output data. The fact that
such dimensionality reduction can lead to improved performance [5] may at "rst appear
somewhat paradoxical, since it cannot increase the information content of the input data,
and in most cases will reduce it. For monitoring of machining processes the sensor signals
typically contain much noise and it is desirable to extract the features that represent the
characteristics of the process (information) from a time series which contains much redund-
ant data. Some care is required in this process since some features in the sensor signals are
correlated with certain levels of tool wear but not with others [6].
Commercially available systems for wear and breakage detection typically set limits for
force or power based on empirical data [7, 8] or are able to adapt to changing force signals
from new and worn tools, in a learning mode [9]. When the measured force or spindle
current falls outside these predetermined "xed limits the tool is assumed to have failed due
to excessive wear or breakage. The disadvantage of the "xed force limit method is that all
the machining conditions must remain nearly identical throughout the whole cutting
operation, and therefore this is applicable only in very simple cases. There are ways to
enhance this type of approach; Lee et al. [10] used the ratio of the cutting forces and
a neural network to classify the tool wear and Rao [11] employed the ratio between the
force and vibration amplitudes to yield a wear index.
This work is based on a series of experiments where a turning operation was monitored
using the following sensors [1]:
E Cutting forces (feed and tangential).
E Spindle current.
E Audible emission.
E Machine vibration.
This work utilised a hybrid approach to the classi"cation of the tool wear level via
a combined ES (based on Taylor's law) and two neural networks (based on adaptive
resonance theory and the self-organising map). Silva et al. [1] demonstrated that it was
possible to classify the tool wear level very accurately for a "xed set of cutting conditions
using one cutting tool on one material. However, it is of interest to examine the tolerance of
this trained hybrid system to changes in cutting conditions. The current work aims to
investigate the &zone of in#uence' of the system by changing the cutting conditions and
determining where the sensor-based part of the hybrid system breaks down.
2. EXPERIMENTAL DESIGN
Sensor data corresponding to di!erent tool wear levels were acquired by machining a bar
of mild steel, with a coated cemented carbide insert. The set of parameters measured wereTHE ADAPTABILITY OF A TOOL WEAR MONITORING SYSTEM 289
TABLE 1
Instrumentation
Sensor Description Mounting
Accelerometer Kistler 8752A50 and Piezotron Base of the turning centre, to
Coupler-Kistler 5108 measure whole-body vibrations
Microphone ECM-1028, matching ampli"er Tool post, directed at the insert
Strain gauges Two half Wheatstone bridges, Feed and tangential direction
constructed from one strain
gauge per side of the tool holder
Current meter CNC built-in sensor
Figure 1. Schematic view of lathe and sensor set.
the vertical vibration of the turning centre, the sound emission whilst cutting, two com-
ponents of cutting force and the spindle current. The instruments used to make these
measurements are described in Table 1.
The turning operation was carried out on a MT 50 CNC Slant Bed Turning Centre with
the experimental set-up and instrumentation is shown in Fig. 1. The analogue signals were
sampled with an Amplicon PC-30PGL data-acquisition board at a sample rate of 20 kHz
per channel for a time period of 26 ms. Data were acquired at intervals of 2 min of cutting
time at which point tool wear was also measured, taking into account an expected life of
about 15 min for the inserts. Six di!erent inserts were used at each condition (to construct
test and con"rmation sets) and three di!erent wear levels were de"ned; new, half-worn
(290 R. G. SILVA ET AL.
TABLE 2
Data analysis applied to each sensor signal
Signal Data analysis
Sound and vibration FFT, average, absolute deviation, kurtosis and skewness
Cutting forces
Spindle current Average, absolute deviation, kurtosis and skewness
TABLE 3
Feature description
Feature Processing
Average Mean value of 512 points
Absolute deviation Absolute deviation of 512 points
Skewness Skewness of 512 points
Kurtosis Kurtosis of 512 points
Energy in two frequency bands Bands 2.2}2.4 kHz and 4.4}4.6 kHz selected from a 512
points FFT
only &strong' features, such as the cutting forces and spindle current that were previously
found to correlate most with the tool wear level. Table 3 describes the way in which the
features were derived from the data. These features were then passed directly to the two
neural networks for classi"cation, with the training data coming from four wear tests and
the testing data used being from two wear tests that were not used during the training phase.
The cutting conditions investigated during the training phase were selected so that the
tool would wear under realistic production conditions that consisted of a cutting speed of
350 m/min, a feed rate of 0.25 rev/min and a depth of cut of 1 mm [14].
In order to assess the &zone of in#uence' of the tool wear monitoring system, systematic
experiments were conducted to investigate as large a range of cutting conditions as possible
for this tool}workpiece combination [14]. This was achieved by varying the cutting
conditions in both a "ne and coarse manner (Table 4) about the cutting conditions used
during the training experiment. Data from three tool states (THE ADAPTABILITY OF A TOOL WEAR MONITORING SYSTEM 291
TABLE 4
¸ist of cutting conditions tested
Test no.R Feed (mm/rev) Speed (m/min) Depth (mm)
0 0.250 350 1.000
1 0.275 350 1.000
2 0.250 350 1.500
3 0.200 350 1.000
4 0.225 350 1.000
5 0.250 350 1.250
6 0.250 337 1.000
7 0.250 350 1.125
8 0.300 350 1.125
9 0.200 350 1.125
10 0.250 344 1.000
11 0.300 344 1.000
12 0.250 344 1.125
13 0.300 344 1.125
14 0.275 344 1.000
15 0.225 344 1.000
16 0.225 350 1.125
17 0.275 350 1.125
18 0.225 344 1.125
19 0.275 344 1.125
20 0.300 350 1.000
21 0.250 325 1.000
22 0.250 344 1.000
23 0.200 344 1.125
24 0.175 350 1.000
25 0.325 350 1.000
R Used to assist discussion.
3.1. FIXED CUTTING CONDITIONS
The results from training the system at a "xed set of cutting conditions have already been
published [1] and so only a summary will be presented here.
Figures 2 & 3 demonstrate that both the SOM and ART2, acting independently, have
a large capacity to categorise the di!erent wear stages. The duration of the training time
e!ected the performance of the SOM more than the ART2, although at the classi"cation
stage both have similar classi"cation speeds, since the basic calculations are relatively
simple.
Of the two networks the SOM, was better able to extract the complex relationship
between tool wear and the selected features and was less prone to the in#uence of noise. This
was possibly due to there being more graduation on the wear scale with the SOM, whereas
the ART2 was subject to the number of classes created during training. To increase the
accuracy of the ART2 it would be necessary to reduce the vigilance parameter, which
controls how "ne the division between classes is.
3.2. VARIABLE CUTTING CONDITIONS
The results obtained from each sensor demonstrated that there were some subtle and
some less subtle variations in the sensor output as cutting conditions changed, as might be292 R. G. SILVA ET AL.
Figure 2. Neural network performance for training data: ( E ) measured, (䉫) ART2, (䊊) SOM.
Figure 3. Neural network performance for test data: ( 䊉 ) measured, (䉫) ART2, (*) SOM.
expected. In the interests of brevity these results have been omitted from this work as the
adaptability of the monitoring system under varying cutting conditions primarily involved
assessing the performance of the neural networks. The results show that although there was
degradation in the classi"cation performance, there was a zone that surrounded the training
conditions where the neural networks worked e!ectively. As might be expected, the
strongest features (feed and tangential force) were the factors that in#uenced network
performance the most. The performance could probably be improved if a selection of points
at various cutting conditions were added to the training set, but this might become a very
expensive data-gathering exercise unless an assessment of the zone of in#uence could be
made.THE ADAPTABILITY OF A TOOL WEAR MONITORING SYSTEM 293
Figure 4. Map of capacity of SOM to generalise for varied cutting conditions.
Performance measurements were obtained by averaging two consecutive samples from
each test condition (Table 4) with individual sample performance being calculated as the
percentage error of the prediction compared to the actual wear value. The maximum
percentage error at each wear level was chosen as the "nal performance measure. The
system was capable of generalising over a certain range of cutting conditions and this
capacity di!ered slightly between the two neural networks as will be shown in the following
sections.
3.3. SELF ORGANISING MAP GENERALISATION FOR VARIABLE CUTTING CONDITIONS
Figure 4 shows the results of the ability of the SOM to generalise when tested at di!erent
cutting conditions than those for which it had been trained. The reference condition can be
seen as a black sphere at the cutting conditions; 350 m/min cutting speed, 0.25 mm/rev feed
rate and 1 mm depth of cut.
As can be seen, classi"cation was successful ('80%) for variations in feed between
0.2 and 0.275 mm/rev with the other cutting conditions kept constant. For increases in the
depth of cut up to 1.175 mm, classi"cation was successful but decreased sharply afterwards.
Cutting speed changes also resulted in reduced performance of the SOM, only one set of
cutting conditions achieving 60% classi"cation with speeds of 344 m/min (Test 14).
Generally, the further away from the reference cutting conditions the worse the classi"ca-
tion result becomes. Figure 5 summarises the deterioration pro"les for each of the condi-
tions, holding remaining conditions at the reference values. The deterioration rates for
individual cutting conditions are: cutting speed 7%/(m/min), feed rate 10%/(0.01 mm/rev),
and depth of cut 20%/(0.1 mm).
3.4. ADAPTIVE RESONANCE THEORY GENERALIZATION FOR VARIABLE CUTTING CONDITIONS
Figure 6 shows the results of exposing the ART2 to the same variation in cutting
conditions as for the SOM. Here, variations in feed rate between 0.23 and 0.285 mm/rev
result in a classi"cation success greater than 80%. The ART2 was successful in classifying
patterns with depths of cut up to 1.225 mm, but the performance deteriorated rapidly
beyond this. Cutting speed signi"cantly in#uences the ART2 performance, it being unable294 R. G. SILVA ET AL.
Figure 5. SOM capacity to generalise by cutting condition.
Figure 6. Map of capacity of ART2 to generalise for varied cutting conditions.
Figure 7. ART2 capacity to generalise by cutting condition.
to generalise when presented with data acquired at cutting speeds lower than 344 m/min. At
some isolated cutting conditions (e.g. Test 5), the ART2 succeeded in classifying the test
samples. But general performance was constrained to a limited zone of in#uence.
Figure 7 illustrates the deterioration pro"les for each condition for the ART2.
The deterioration rates are approximately; cutting speed 100% after 344 m/min, feed
rate asymmetrical for feed '0.275 mm/rev, 100% and for feed (0.25 mm/rev,
10%/(0.01 mm/rev), depth of cut 20%/(0.1 mm) average.THE ADAPTABILITY OF A TOOL WEAR MONITORING SYSTEM 295
TABLE 5
Zone of in-uence for a classi,cation greater than 80%
SOM ART2
Feed rate (mm/rev) 0.21}0.27 0.23}0.285
Cutting speed (m/min) 347}350 342}350
Depth of cut (mm) 1}1.1 1}1.05
In summary, both algorithms achieved similar generalisation capabilities, which resulted
in a classi"cation success greater than 80% within a common zone of in#uence, as
summarised in Table 5.
4. DISCUSSION
Ideally, the tool monitoring system should be relatively independent of the cutting
conditions, but as has already been observed this is not the case. The experiments have
shown that the sensor-based component of a hybrid system shows a modest, but useful,
range of cutting conditions over which its performance will not degrade to an unacceptable
level. At worst, this type of information could be used to assess the minimum necessary
number of test and training points required to cover all foreseeable cutting conditions. Also,
if the e!ect of cutting conditions on the sensor signals can be modelled (however empiric-
ally) a level of adaptability could be built into the system by modifying the feature values to
account for variation in cutting conditions.
For example, if the depth of cut is increased, the area of the chip}tool contact will increase
approximately in proportion to the change in depth of cut, and this probably results in the
increase in the components of cutting force. It might be possible to remove the e!ect of
depth of cut by use of a model, but most models are very speci"c and usually only estimate
cutting force for a new tool and neglect the rate at which it increases with wear. It would be
simpler to normalise the forces dividing it by the depth of cut.
For machine vibration, sound and dynamic components of force it would be expected
that the vibrational modes of the machine tool would dominate so that di!erent forcing
functions (for example from di!erent depths of cut) would result in di!erent evolutions.
However, as the depth of cut increases the vibration appears to reduce, as shown by the
evolution of both the absolute deviation and frequency spectra of sound and vibration
(Fig. 8). This e!ect is possibly due to an increase of the cutting forces leading to increased
stability of the cutting process. Irrespective of the causes, Fig. 8 shows that it would, in
principle, be possible to improve network performance using an empirical model.
An increase in feed rate results in a higher metal removal rate and higher forces at the tool
rake as well as an increase in compressive stress thus giving rise to an increase in the cutting
forces. The force gradient changes slightly at a feed rate of around 0.275 mm/rev for both
tangential and feed forces. At this feed rate, the tool appears to gain stability (Fig. 9) with an
accompanying substantial reduction in the wear rate. Again, these changes are seen in the
sensor outputs and these changes could, in principle, be used to adapt the network.
All sensors seem to be a!ected in di!erent ways with changes in the feed rate. The spectra
of both sound and vibration have shown non-monotonic relationships with tool wear as do
the other statistical features. The change in behaviour of the force as well as the other
features at a feed rate of 0.275 mm/rev may be due to the e!ect illustrated in Fig. 10 where296 R. G. SILVA ET AL. Figure 8. E!ect of depth of cut on force magnitude and absolute deviation of sound and vibration, worn tool (Relative Scales): (䉬) feed force, (䡵) tangential force, (*) Abs dev of sound, (䉫) Abs dev of vibration. Figure 9. Force magnitude and frequency band 1, worn tool: (䉬) feed force, (䡵) tangential force, (*) sound band 1, (䉫) vibration band 1. an increase in the feed rate leads to part of the material being left on the workpiece. With a further increase in the feed rate it would be expected for the forces to drop, an e!ect that would also be di!erent in new and worn tools. The networks were also least tolerant to changes in feed rate (Figs 5 and 7) and this condition is therefore one in which training of the networks would have to be intense. It might be noted, however, that the changes in feed rate investigated are larger in percentage terms than in the other cutting conditions. Increasing the cutting speed will result in an overall increase in cutting torque. Changes in the average cutting forces due to cutting speed seem to be a!ected di!erently with di!erent cutting speeds, showing no obvious evolution with cutting speed. However, the dynamic changes with cutting speed show a clearer evolution. The absolute deviations of sound and vibration signals as well as changes in the frequency band 2.2}2.4 kHz show a good correlation with cutting speed. For the higher frequency band (4.2}4.6 kHz) only the vibration signal was a!ected signi"cantly, increasing with the cutting speed. The response in
THE ADAPTABILITY OF A TOOL WEAR MONITORING SYSTEM 297
Figure 10. Material removal rate changes with increased feed rate (not to scale).
this band probably has more to do with the #exibility of the machine and tool than with the
cutting process directly.
It has been demonstrated [13] that the vibration characteristics of the machine and tool
a!ect the performance of features, as these characteristics are in#uenced by the cutting
conditions and tool shank properties (e.g. dimensions and tool post-sti!ness). In many
cases, the changes in features resulting from changing cutting conditions were much larger
than those occurring with wear making it di$cult to de"ne a set of signal processing
routines that would isolate the tool wear changes independently. This has signi"cant
implications for a tool wear monitoring system probably resulting in a monitoring system
that is speci"c to a small working set of conditions. However, this might not be as limiting as
one might expect since many #exible cells use only a small set of conditions. In a more
sophisticated application, it may indeed be possible for a system to learn the vibration
characteristics of the machine tool by running a simple series of tests with a new tool.
To overcome some of the limitations necessary to extend the adaptability of the system to
cutting condition variations it would be worth implementing the following in a practical
application:
E Normalise the cutting forces, e.g. feed force normalised for depth of cut.
E Account for workpiece diameter, which is readily available from the CNC machine.
E Care in the use of feed rate values as large feed rates a!ect the cutting force.
As far as the relative capabilities of the two neural networks are concerned, Figs 2 and 3
demonstrate that both the SOM and ART2, each acting alone, are able to categorise the
di!erent wear stages. The training period had a large e!ect on the performance of the SOM
and more training time was required for the SOM than the ART2, although at the
interpretation stage both have similar speeds as the basic calculations are relatively simple.
Of the two networks the SOM was better able to extract the complex relationship
between tool wear and the selected features, it was less prone to the in#uence of noise and
was able to generalise more completely. This was due to the fact that, with the SOM, more
graduations on the wear scale were possible given that each neurone in the 6;6 matrix of
the Kohonen layer could tune to a di!erent wear level, whereas the ART2 was subject to the
number of classes created during training. To increase the accuracy of the ART2 it would be
necessary to reduce the vigilance parameter which controls how "ne the classes are.298 R. G. SILVA ET AL.
5. CONCLUSIONS
An on-line tool wear monitoring system for turning operations was developed and
evaluated in an earlier study [1] and the current work is related to the applicability of this
system in realistic situations where the cutting conditions may change.
The earlier study established that tool wear can be e!ectively detected under "xed cutting
conditions using features of the cutting force, sound, vibration and spindle current. The
system employed two arti"cial neural networks for learning the characteristics of the signals
from multiple sensors, and was supervised by an Expert System based on a Taylor model,
which was used to detect and disregard obvious misclassi"cations.
Both neural networks have demonstrated an ability to classify tool wear under a "xed set
of cutting conditions. When asked to classify tool wear under di!erent cutting conditions
their performance was radically reduced, although there was a zone that surrounded the
training conditions where the neural networks worked e!ectively. As might be expected, the
strongest features (feed and tangential force) were the factors that in#uenced network
performance the most.
This work has served to quantify the grid spacing of training conditions required in
a truly general system and to suggest the decay rate in performance around each of the
training points. This allows an optimisation of the training strategy for more adaptive
systems.
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