Tài liệu Computer-Aided.Design.Engineering.and.Manufacturing P5 ppt

Li, Xiaoli "Fuzzy Neural Network and Wavelet for Tool Condition Monitoring"
Computational Intelligence in Manufacturing Handbook
Edited by Jun Wang et al
Boca Raton: CRC Press LLC,2001

©2001 CRC Press LLC

15

Fuzzy Neural Network
and Wavelet for Tool

Condition Monitoring

15.1 Introduction

15.2 Fuzzy Neural Network

15.3 Wavelet Transforms

15.4 Tool Breakage Monitoring with Wavelet Transforms

15.5 Identification of Tool Wear States Using
Fuzzy Methods

15.6 Tool Wear Monitoring with Wavelet Transforms
and Fuzzy Neural Network


15.1 Introduction

To reduce operating costs and improve product quality are two objectives for the modern manufacturing
industries, so most manufacturing systems are fast converting to fully automated environments such as
computer integrated manufacturing (CIM) and flexible manufacturing systems (FMS). However, many
manufacturing processes involve some aspects of metal cutting operations. The most crucial and deter-
mining factor to successful maximization of the manufacturing processes in any typical metal cutting
process is tool condition. It would seem be logical to propose that tool condition monitoring (TCM)
will inevitably become an automated feature of such manufacturing environments. Due to failure, cutting
tools adversely affect the surface finish of the workpiece and damage machine tools; serious failure of
cutting tools may possibly endanger the operator’s safety. Therefore, it is very necessary to develop tool
condition monitoring systems that would alert the operator to the states of cutting tools, thereby avoiding
undesirable consequences [1].
Initial TCM systems focused mainly on the development of mathematical models of the cutting process,
which were dependent upon large amounts of experimental data. Due to the complexity of the metal
cutting process, an accurate model for wear and breakage prediction of cutting tools cannot be obtained,
so that many researchers resort to sensor integration methods for replacing model methods. These results
in a series of problems such as signal processing, feature extraction, and pattern recognition. To overcome
the difficulty of these problems, computational intelligence (fuzzy systems, neural networks, wavelet
transforms, genetic algorithms, etc.) has been applied in some TCM systems in recent years. The TCM
systems based on computational intelligence, such as wavelet transforms [2], fuzzy inference [3–5], fuzzy
neural networks [6–9], etc., have been established, in which all forms of tool condition can be monitored.
Fuzzy systems and neural networks are complementary technologies in the design of intelligent systems.
Neural networks are essentially low-level computational structures and algorithms that offer good per-
formance in dealing with sensory data, while fuzzy systems often deal with issues such as reasoning on

Xiaoli Li

Harbin Institute of Technology

©2001 CRC Press LLC

a higher lever than neural networks. However, since fuzzy systems do not have much learning capability,
it is difficult for a human operator to tune the fuzzy rules and membership functions from the training
data set. Also, because the internal layers of neural networks are always opaque to the user, the mapping
rules in the network are not visible so that it is difficult to understand; furthermore, the convergence
(learning time) is usually very slow or not guaranteed. Thus, it is very necessary to reap the benefits of
both fuzzy systems and neural networks by combining them in a new integrated system, called a fuzzy
neural network (FNN). FNN had been widely used in the TCM [10–12].
Spectral analysis and time series analysis are the most common signal processing methods in TCM.
These methods have a good solution in the frequency domain but a very bad solution in the time domain,
so that they lose some useful information during signal processing. In general, they are recommended
only for processing stability stochastic signals. Recently, wavelet transforms (WT) have been proposed as
a significant new tool in signal analysis and processing [13, 14]. They have been used to analyze some
signals for tool breakage monitoring [15, 16]. WT has a good solution in the time–frequency domain so
that it can extract more information in the time domain at different frequency bands from any signals [17].
Tool condition monitoring can be divided into the two types: tool breakage and tool wear. This chapter
addresses how to apply the fuzzy neural network and wavelet transforms to TCM. First, the fuzzy neural
network and the wavelet transforms are respectively introduced. Second, the continuous wavelet trans-
forms (CWT) and discrete wavelet transforms (DWT) are used to decompose the spindle AC servomotor
current signal and the feed AC servomotor current signal in the time–frequency domain, respectively.
Real-time tool breakage detection of small-diameter drills is presented by using motor current decom-
posed. Third, analyzing the effects of tool wear as well as cutting parameters on the current signals, the
models of the relationship between the current signals and the cutting parameters are established, and
the fuzzy classification method is effectively used to detect tool wear states. Finally, wavelet packet
transforms are applied to decompose AE signals into different frequency bands in the time domain; the
root means square (RMS) values extracted from the decomposed signals of each frequency band are
referred to as the features of tool wear. The fuzzy neural network is presented to describe the relationship
between the tool wear conditions and the monitoring features.

15.2 Fuzzy Neural Network

15.2.1 Combination of Fuzzy System and Neural Network

Fuzzy system (FS) and neural networks (NN) are powerful tools for controlling the complex systems
operating under a known or unknown environment. Fuzzy systems can easily be used to express approx-
imate knowledge and to quickly implement a reaction, but have difficulty in executing learning processes
[18]. Neural networks have strong learning abilities but are weak at expressing rule-based knowledge.
Although the fuzzy system and neural networks possess remarkable properties when they are employed
individually, there are great advantages to using them synergistically, resulting in what are generally
referred to as

neuro-fuzzy approaches

[19].
Neural networks are organized in layers, each consisting of neurons or processing elements that are
interconnected. The neurons or perceptions compute a weight sum of their inputs, generating an output.
The connections between the neurons have weighted numerical inputs associated with them. There are
a number of learning methods to train neural nets, but the backpropagation (BP) paradigm has emerged
as the most popular training mechanism. The BP method works by measuring the difference between
the system output and the observed output value. The values being calculated at the output layer are
propagated to the previous layers and used for adjusting the connection weights. But there are potential
drawbacks: (i) no clear guidelines on how to design neural nets; (ii) accuracy of results relies heavily on
the size of the training set; (iii) the logic behind the estimate is hard to convey to the user; (iv) long
learning time; (v) local convergence. In order to overcome its drawbacks, some hybrid models of neural
network and fuzzy system are presented. There are many possible combinations of the two systems, but
the four combinations shown in Figure 15.1 have been widely applied to actual systems [20].

©2001 CRC Press LLC

Figure 15.1(a) shows the case where one piece of equipment uses the two systems for different purposes
without mutual cooperation. The model in Figure 15.1(b) shows NN used to optimize the parameters

of FS by minimizing the error between the output of FS and the given specification. Figure 15.1(c) shows
a model where the output of FS is corrected by the output of NN to increase the precision of the final
system output. Figure 15.1(d) shows a cascade combination of FS and NN where the output of the FS
or NN becomes the input of another NN or FS. The models in Figures 15.1(b) and 15.1(c) are referred
to as a combination model with net learning and a combination model with equal structure, respectively.
These are shwon in greater detail in Figure 15.2. Figure 15.2(a) shows that the total system is controlled
by means of fuzzy system, but the membership of the fuzzy system is produced and adjusted by the
learning power of the neural network. The model in Figure 15.2(b) shows that the fuzzy system can be
controlled by the neural network; the inference processing of the fuzzy system is responded to by the
neural network.

15.2.2 Fuzzy Neural Network

In this chapter, a new neural network with fuzzy inference is presented. Let

X

and

Y

be two sets in [0,1]
with the training input data (

x

1

, x

2

,

. . . , x

n

) and the desired output value (

y

1

,

y

2

,

. . . , y

m

), respectively.
The set of the corresponding elements of the weight matrix is (

w

11

,

w

12

, . . . ,

w

nm

). Based on the fuzzy
inference, the definition is given as follows:
Equation (15.1)
and

y

j



= max(min(

x

i

,

w

ij

)) (

i

= 1, 2,

…

,

n

;

j

= 1, 2, . . . ,

m)

Equation (15.2)
The fuzzy neural network topology is shown in Figure 15.3. Basically, the idea of backpropagation

(BP) is used to find the errors of node outputs in each layer. Without any loss of generality, the detailed
learning processes of a single layer for clarity are derived as follows. The derivation can easily be extended
to the multiple-output case. The goal of the proposed learning algorithm is to minimize a least-squares
error function:
Equation (15.3)

FIGURE 15.1

Combination type of neural network and fuzzy system. (Reprinted with permission of Springer-Verlag
London, Ltd. From “Hybrid Learning for Tool Wear Monitoring,”

Int. J. Adv. Manuf. Technol.

, 2000, 16, 303–307.)
FS
FS
FS
FS
NN
NN
NN
NN
(a)
(c)
(d)
(b)
YXW= o
ETO
jj
=

()
–/
2
2

©2001 CRC Press LLC

FIGURE 15.2

Combination model with (a) net learning, and (b) equal structure. (Reprinted with permission of
Springer-Verlag London, Ltd. From “Hybrid Learning for Tool Wear Monitoring,”

Int. J. Adv. Manuf. Technol.,

2000,
16, 303–307.)

FIGURE 15.3

FNN net topology. (Reprinted with permission of Chapman & Hall, Ltd. From “On-line Tool Condition
Monitoring System with Wavelet Fuzzy Neural Network,”

Journal of Intelligent Manufacturing

, 1997, 8, 271–276.)
NN
input
output
(a)
FS

if
then
output
input
(b)
y
m
x
n
x
2
x
1
y
2
y
1
W
nm
W
1m
W
1l

©2001 CRC Press LLC

where

O

j

= max(min(

x

i

,

w

ij

)),

T

j

is desired output values,

O

j

is the actual values, the least-squares error
between them is

E

. The general parameter learning rule used is as follows:
Equation (15.4)
where
Equation (15.5)
Set
Equation (15.6)
Define
when ,
otherwise
when otherwise

a

2

= x

s



Assuming
Equation (15.7)
According to fuzzy min–max and smooth derivative ideas, a fuzzy ruler is constructed as follows:
Equation (15.8)
and
∂
∂
=

∂
∂
⋅
∂
∂
E
w
E
O
O
w
ij j
j
ij
∂
∂
=
∂∨ ∧
()
()
∂∧
()
∂∧
()
∂
O
w
xw
xw
xw

w
j
ij
iij
ssj
ssj
sj
,
,
,
a
xw
xw
xw xw
xw
a
xw
w
iij
ssj
ssj
is
iij
ssj
ssj
sj
1
2
=
∂∨ ∧

()
()
∂∧
()
=
∂∨ ∧
()
∨∧
()
()






∂∧
()
=
∂∧
()
∂
≠
,
,
,,
,
,
∧
()

≥∨ ∧
()
()
=
≠
xw xw a
ssj
is
iij
,,,
1
1
axw
ssj1
=∧
()
, ;
xwa
ssj
≥=,
2
1,
∂
∂
=
O
w
j
sj
∆

if and then
if and then
if and then
if and then
xw x xw x
xw x xw x
xw w xw
xw w xw
ssj s
is
iij s
ssj s
is
iij s
ssj sj
is
iij
ssj sj
is
iij
<≥∨∧
()
()
=
<<∨∧
()
()
=
≥≥∨∧
()

()
=
≥<∨∧
()
()
=
≠
≠
≠
≠
,
,
,
,
∆
∆
∆
∆
2
1
ww
s

©2001 CRC Press LLC

Equation (15.9)
Set
Equation (15.10)
Then
Equation (15.11)

the changes for the weight will be obtained from a



δ

-rule with expression
Equation (15.12)
where

µ

is learning rates ,

µ

∈

[0,1].
To test the fuzzy neural network (FNN), it is compared with the BP neural networks (BPNN) [22].
Under the same conditions (training sample, networks structure (5

×

5), learning rate (0.8), convergence
error (0.0001)), the training iteration of FNN is 7, but that of BPNN is 427. Figure 15.4 shows each
training process.

FIGURE 15.4

(Top): Training process BPNN and (Bottom): FNN. (Reprinted with permission of Chapman & Hall,
Ltd. From “On-line Tool Condition Monitoring System with Wavelet Fuzzy Neural Network,”

Journal of Intelligent
Manufacturing

, 1997, 8, 271–276.)
∂
∂
=
()
E
O
TO
i
jj
––
δ
j
j
E
O
=
∂
∂
∂
∂
=
E
w

ij
j
δ∆
∆∆
w
ij j
=
µδ
0.06
0.04
0.02
0 50 100 150 200 250 300 350 400 450
0
Iteration
Error
0
0
1 2345678
1.2
0.9
0.6
0.3
Error
Iteration

©2001 CRC Press LLC

15.3 Wavelet Transforms

15.3.1 Wavelet Transforms (WT)

An energy limited signal

f

(

t

) can be decomposed by its Fourier transforms

F

(

w

), namely
Equation (15.13)
where
Equation (15.14)

f

(

t

) and

F

(

w

) are called a pair of Fourier transforms. Equation 15.13 implies that

f

(

t

) signal can be
decomposed into a family in which harmonics

e

iwt

and the weighting coefficient

F

(

w

) represent the

amplitudes of the harmonics in

f

(

t

).

F

(

w

) is independent of time; it represents the frequency composition
of a random process that is assumed to be stationary so that its statistics do not change with time.
However, many random processes are essentially nonstationary signals such as vibration, acoustic emis-
sion, sound, and so on. If we calculate the frequency composition of nonstationay signals in the usual
way, the results are the frequency composition averaged over the duration of the signal, which can’t
adequately describe the characteristics of the transient signals in the lower frequency.
In general, a short-time Fourier transform (STFT) method is used to deal with nonstationary signals.
STFT has a short data window centered at time (see Figure 15.5).
Spectral coefficients are calculated for this short length of data, and the window is moved to a new
position and repeatedly calculated. Assuming an energy limited signal,

f

(t) can be decomposed by STFT,

namely
Equation (15.15)
where

g

(

t

−

t

0

) is called

window function

. If the length of the window is represented by time duration

T

,
its frequency bandwidth is approximately 1/

T

. Use of a short data window means that the bandwidth of

each spectral coefficient is on the order 1/

T

, namely its frequency band is wide. A feature of the STFT is
that all spectral estimates have the same bandwidth. Clearly, STFT cannot obtain a high resolution in
both the time and the frequency domains.

FIGURE 15.5

An illustration of the STFT. (Reprinted with permission of Elsevier Science, Ltd. From “Tool Wear
Monitoring with Wavelet Packet Transform-Fuzzy Clustering Method,”

Wear,

1998, 219(2), 145–154.)
f(t)
t
t
0
g(t-t
0
)
ft Fwe dt
iwt
()
=
()
∞
+∞

∫
1
2
π
–
Fw f te dt
iwt
()
=
()
∞
+∞
∫
–
–
Gwt f t gt t e dt
R
iwt
,–
–
00
()
=
() ( )
∫

©2001 CRC Press LLC

Wavelet transforms involve a fundamentally different approach. Instead of seeking to break down a
signal into its harmonics, which are global functions that go on forever, the signals are broken down into

a series of local basis functions called

wavelets

. Each wavelet is located at a different position on the time
axis and is local in the sense that it decays to zero when sufficiently far from its center. At the finest scale,
wavelets may be very long. Any particular local features of signals can be identified from the scale and
position of the wavelets. The structure of nonstationary signals can be analyzed in this way, with local
features represented by a close-packet wavelet of short length.
Given a time varying signal

f

(

t

), wavelet transforms (WT) consist of computing a coefficient that is
the inner product of the signal and a family of wavelets. In the continuous wavelet transforms (CWT),
the wavelet corresponding to scale

a

and time location

b

is
Equation (15.16)
where

a

and

b

are the dilation and translation parameters, respectively.
The continuous wavelet transform is defined as follows:
Equation (15.17)
where “*” denotes the complex conjugation.
With respect to

w

f

(

a,b

), the signals

f

(

t

) can be decomposed into

Equation (15.18)
where

c

ψ

is a constant depending on the base function. Similar to the Fourier transforms,

w

f

(

a,b

) and

f

(

t

) constitute a pair of wavelet transforms. Equation 15.17 implies that WT can be considered as

f

(

t

)
signal decomposition. Compared with the STFT, the WT has a time-frequency function that describes
the information of

f

(

t

) in various time windows and frequency bands.
When

a

= 2

j

,

b

=

k

2

j

,

j, k ∈

Z, the wavelet is in this case
Equation (15.19)
The discrete wavelet transform (DWT) is defined as follows:
Equation (15.20)
where c
j,k
is defined as the wavelet coefficient, it may be considered as a time–frequency map of the
original signal f(t). Multi-resolution analysis is used in discrete scaling function:
Equation (15.21)
Set
Equation (15.22)
ψψ
ab
a
b
a
,
–
=
()
1
1

ab Ra,,∈≠0
wab xt tdt
fab
,
,
*
()
=
() ()
∫
ψ
ft
c
w a b dadb
f
a
b
a
()
=
()
()
+∞
∞
+∞
∫∫
1
0
1
1

ψ
ψ
–
–
,
ψψ
jk
d
j
tk
,
=
()
−
2
2
–
–
cftt
jk jk,,
=
() ()
∫
ψ
φφ
jk
d
tk
d
d

,
=




−
2
2
2
2
–
dfttdt
jk jk,
=
() ()
∫
φ
,
*
©2001 CRC Press LLC
where d
j,k
is called the scaling coefficient, and is the sampled version of original signals. When j = 0, it
is the sampled version of the original signals. Wavelet coefficients c
j,k
(j = 1, 2, . . . , J ) and scaling
coefficients d
j,k
are given by

Equation (15.23)
and
Equation (15.24)
where x[n] are discrete-time signals, is the analysis discrete wavelets, and the discrete
equivalents to , are called scaling sequence. At each resolution j
> 0, the scaling coefficients and the wavelet coefficients can be written as follows:
Equation (15.25)
Equation (15.26)
In fact, the structure of computations in DWT is exactly an octave-band filter [23]. The terms g and
h can be considered as high-pass and low-pass filters derived from the analysis wavelet
ψ
(t) and the
scaling function
φ
(t), respectively.
15.3.2 Wavelet Packet Transforms
Wavelet packets are particular linear combinations of wavelets. They form bases that retain many of the
orthogonality, smoothness, and location properties of their parent wavelets. The coefficients in the linear
combinations are computed by a factored or recursive algorithm, with the result that expansions in
wavelet packet bases have low computational complexity.
The discrete wavelet transforms can be rewritten as follows:
Equation (15.27)
Set
Equation (15.28)
cxnhnk
jk
n
j
j
,

=
[]
[]
∑
– 2
dxngnk
jk
n
j
j
,
=
[]
[]
∑
– 2
hn k
j
j
– 2
[]
222
2–/ –
–
jjj
tk
ψ
()





gn k
j
j
– 2
[]
cgnkd
jk
n
jk+
=
[]
∑
1
2
,,
–
dhnkd
jk
n
jk+
=
[]
∑
1
2
,,
–
cft htc ft

dft gtc ft
cft ft
jj
jj
()
[]
=
() ()
[]
()
[]
=
() ()
[]
()
[]
=
()
*
*
–
–
1
1
0
Hhkt
Ggkt
k
k
⋅

{}
=
()
⋅
{}
=
()
∑
∑
–
–
2
2
©2001 CRC Press LLC
Then Equation 15.27 can be written as follows:
Equation (15.29)
Clearly, DWT is only the approximation c
j-1
[f(t)] but not the detail signals d
j-1
[f(t)]; wavelet packet
transforms don’t omit the detail signals. Therefore, wavelet packet transforms is expressed as follows:
Equation (15.30)
Let Q
j
i
(t) be the i
th
packet on j
th

resolution, then the wavelet packet transforms can also be computed
by the recursive algorithm, as follows:
Equation (15.31)
where t = 1, 2, . . . , 2
J-i
, i = 1, 2, . . . , 2
j
, j = 1, 2, . . . , J, J = log
2
N, N is data length. The wavelet packet
transforms are represented by Figure 15.6.
15.4 Tool Breakage Monitoring with Wavelet Transforms
Tool breakage monitoring plays an important role in the cutting process. These monitoring systems have
been developed over many years. A fair amount of techniques have been developed to detect tool breakage
during the cutting process; the most common techniques reported in the industrial machining environ-
ment include force, acoustic emission (AE), and current. It is known that force measurement is the best
method for detecting tool breakage. However, its main disadvantage is that each tool is required to a
fitted sensor system, resulting in a very high cost; in addition, the installation of a force-measuring sensor
system is difficult for the present machine tools. In recent years, the AE sensing technique has also been
considered one of the most effective methods for tool breakage monitoring. One of the main obstacles
is how to detect the AE signals from a rotating tool such as in boring, drilling, and milling. Moreover,
AE signals analysis is a very difficult problem, for example, how to extract the features from AE signals
that are related to tool condition. In Section 15.6, a method is described for using AE signals to monitor
tool wear condition.
During the cutting processes, motor current is related to the tool conditions. Less power is consumed
when a tool is broken, and this variance can be exploited for on-line tool breakage monitoring. The
motor current of machine tools can be measured through a current transformer (such as a Hall Current
Sensor); when the measured signals are processed, the result will be found to drop instantaneously and
soon recover to a level prior to the drop when tool breakage occurs [24]. The current measurement
system is relatively simple, and its mounting will not affect the machining operations [25]. But it is less

sensitive for small tool breakage when compared to force sensing and AE sensing; the system of current
measurement is only reliable in monitoring tool breakage at medium and heavy cuts [26].
This section presents on-line tool breakage detection of small diameter drills by sensing the AC
servomotor current [27]. The continuous wavelet transforms (CWT) were used to decompose the spindle
cft Hc ft
dft Gc ft
jj
jj
()
[]
=
()
[]
{}
()
[]
=
()
[]
{}
–
–
1
1
cft Hc ft Gd ft
dft Gc ft Hd ft
jj j
jj j
()
[]

=
()
[]
{}
+
()
[]
{}
()
[]
=
()
[]
{}
+
()
[]
{}
––
––
11
11
Qt ft
Q t HQ t
Q t GQ t
j
i
j
i
j

i
j
i
0
1
21
1
2
1
()
=
()
()
=
()
()
=
()
–
–
–
©2001 CRC Press LLC
AC servomotor current signals and the discrete wavelet transforms (DWT) were used to decompose the
feed AC servomotor current signals in the time–frequency domain. The features of tool breakage were
extracted from the decomposed signals. Experimental results showed that the proposed monitoring
system could work in real time; in addition, it had a low sensitivity to changes in the cutting conditions
and a high detection rate for the breakage of small diameter drills [28].
15.4.1 Experimental Setup
The schematic diagram of the experimental setup is shown in Figure 15.7. Cutting tests were performed
on a Machine Center Makino-FNC74-A20. The four axles (spindle, X, Y, and Z) of the machine have

recalculating ball screw drives and are directly driven by permanent magnet synchronous AC servomotors.
The AC servomotor current signals of the Machine Center were measured through Hall Current Sensor.
The signals were first passed though low-pass filters (cut-off frequency: 500 HZ) and sent via an A/D
converter to a personal computer.
A successful tool breakage detecting method must be sensitive to tool change in tool condition, but
insensitive to the variations of cutting conditions. Hence, cutting tests were conducted at different
conditions to evaluate the performance of the proposed method. Table 15.1 shows the tool parameters
and cutting conditions.
15.4.2 Wavelet Analysis of Tool Breakage Signals
Figures 15.8(a)and 15.8(b) show the spindle current signal and feed current signals of an AC servomotor,
respectively. Figures 15.9(a) and 15.9(b) show the results of the spindle current CWT and the results of
the feed current DWT at resolution j = 2, respectively.
To detect the tool breakage efficiently in drilling process monitoring, the method must fit the different
kinds of cutting conditions. Figures 15.10, 15.12, and 15.14 show the spindle current signals and feed
current signals under the different cutting conditions, respectively. Figures 15.11, 15.13, and 15.15 are
the results of the above signal processing, respectively. Clearly, small differences between the normal wear
and tool breakage can be observed by processed signals. Using a simple methodology, tool breakage can
be reliably detected.
FIGURE 15.6 Tree structure of the wavelet packet transforms. (Reprinted with permission of Elsevier Science, Ltd.
From “Tool Wear Monitoring with Wavelet Packet Transform-Fuzzy Clustering Method,” Wear, 1998, 219(2),
145–154.)
resolution
packet
number of
packet
data point

in each
j=1
j=2

N/2
N/4
f(t)
2
1
2
2
2
j
N/2
j
Q
1
1
Q
2
1
Q
2
2
Q
2
3
Q
2
4
Q
1
2
©2001 CRC Press LLC

15.5 Identification of Tool Wear States Using Fuzzy Methods
In recent years, indirect methods that rely on the relationship between tool condition and measured
signals (such as force, acoustic emission, vibration, current, etc.) for detecting the tool wear condition
have been extensively studied. Among the methods used for detecting tool wear condition, motor current
sensing constitutes one of major methods. The feasibility of motor power and current sensing for adaptive
control and tool condition monitoring has been described [29]. Mannan and colleagues recommend
using the spindle and feed current measured to estimate the static torque and thrust for monitoring tool
wear condition [30]. The major advantages of using the measured motor current to detect malfunction
in the cutting process are that the measuring apparatus does not disturb the machining process, and it
can be applied in the manufacturing environment at almost no extra cost.
In the chapter, the spindle and feed currents measured are used to estimate the tool wear condition
in boring. It is known that current signals depend on the cutting variable, namely, cutting speed v, feed
speed f, the depth of cut d, as well as on the tool wear w. Moreover, tool wear itself also depends on the
cutting variables. So, the measured currents are affected by the tool wear directly and the cutting variables
indirectly. This section presents a new method to estimate tool wear condition by the current measured.
The models with regression technology are first presented under a wide range of cutting conditions; then
the method is used to classify the tool wear states by fuzzy classification. The key of the method is to
model the relationship between the measured current and the tool wear states under different cutting
conditions. Depending on the relationship, tool wear states can be estimated by known cutting parameters
and current. Finally, a fuzzy inference method is presented to fuse the classification result of spindle and
feed current signals. Experimental results showed that the method could be effectively employed in
practical industry [31].
FIGURE 15.7 Schematic diagram of the experimental setup. (Reprinted with permission of Elsevier Science, Ltd.
From “On-Line Detection of the Breakage of Small Diameter Drills Using Current Signature Wavelet Transform,”
International Journal Machine Tools and Manufacture, 1999, 39, 157–164.)
TABLE 15.1 Experimental Conditions
Tool HSS-drill
Diameter 3mm
Tool material high-speed steel
Cutting Spindle speed 450 r/min

conditions Feed rate 30 mm/min
Without coolant
Workpiece 45# quench steel
©2001 CRC Press LLC
15.5.1 Experimental Setup and Results
Figure 15.16 shows a schematic diagram of the experimental setup. Cutting tests were performed on a
Machine Center Makino-FNC74-A20. The AC servomotor current signals of the Machine Center were
measured through Hall Current Sensor. The signals were first passed though low-pass filters (cut-off
frequency: 500 HZ), and then sent to a personal computer via an A/D converter. Table 15.2 shows the
experimental conditions.
During the experiments, both spindle and feed current amplitude changed because of the change of
tool wear, spindle speed, feed speed, and the depth of cut. The main conclusions are as follows:
1. Both spindle and feed current increase as tool wear increases; this is due to the increase of friction
between tool and workpiece. Moreover, current increases almost linearly as tool wear. In addition,
we found that tool wear had a more significant effect on feed current than spindle current.
2. Both spindle and feed current increase as the depth of cut increases. Moreover, feed current
increases almost linearly as the depth of cut increases, while spindle current increase is proportional
to the square of the depth of cut.
3. The current signal increases overall as the spindle speed increases, but current fluctuates at the
range of 20 to 30 m/min, see Figure 15.17. The reason for the change of current signals is complex;
the main influence factor is temperature, and the effect of temperature is small at the low speed,
but increases as spindle speed increases.
4. The current signal increases overall as the feed speed increases, and current fluctuates, see Figure
15.18. The reason for the change of current signal is complex; see the discussion in [32].
FIGURE 15.8 (Top): Live tool breakage spindle current signals, cutting speed 250 r/min, feed speed 30 mm/min,
drill diameter 2 mm and (Bottom): Live tool breakage feed current signals, cutting speed 250 r/min, feed speed
30 mm/min, drill diameter 2 mm. (Reprinted with permission of Springer-Verlag London, Ltd. From “Real-Time
Detection of the Breakage of Small Diameter Drills with Wavelet Transform,” Int. Adv. Manuf. Technol., 1999, 14,
539–543.)
5

2.5
-2.5
0
-5
012345
0.5
1.5 2.5 3.5 4.5
tool breakage
Time sec
Amplitude of current V
5
2.5
-2.5
0
-5
0
12
3
4
5
0.5
1.5
2.5
3.5 4.5
tool breakage
Time sec
Amplitude of current V