MSEC2010: Short-time fourier transform method in ae signal analysis for diamond coated failure monitoring in machinging applications

1 Copyright © 2009 by ASME
Proceedings of the ASME 2010 International Manufacturing Science and Engineering Conference
MSEC2010
October 12-15, 2010, Erie, Pennsylvania, USA
MSEC2010-34305
Short-time Fourier Transform Method in AE Signal Analysis for Diamond Coating Failure
Monitoring in Machining Applications
Ping Lu and Y. Kevin Chou
Mechanical Engineering Department
The University of Alabama
Tuscaloosa, AL 35487
Raymond G. Thompson
Vista Engineering, Inc.
Birmingham, AL 35211
ABSTRACT
Coating failures due to delaminations are the primary life-
limiting criteria of diamond-coated tools in machining. Process
monitoring to capture coating failures is thus desired to prevent
from poor part quality and possible production disruption.
Following previous studies of AE signal analysis for diamond
coating failure monitoring in machining applications, this
research applied a short-time Fourier transformation (STFT)
method to capture the coating failure transition during cutting.
The method uses sub-divided signal segments, in a continuous
manner, for the fast Fourier transform (FFT) analysis and
computes the amplitude ratio of high vs. low frequencies as a
function of cutting time during a cutting pass.
The results show that during the coating failure pass, a
clear sharp increase of amplitude ratio (value change over one)
of high/low frequency occurs along the cutting time. On the
other hand, the amplitude ratio only exhibits a certain low range
fluctuations in other passes, e.g., initial cutting and prior to
failure passes. Thus, it can be suggested that the applied STFT
method has a potential for diamond coating failure monitoring.
However, for coating failure associated with a smaller tool
wear (less than 0.8 mm flank wear-land width), the amplitude
ratio plot from the STFT analysis may not clearly show the
failure transition.
INTRODUCTION
Diamond-coated tools made by chemical vapor deposition
(CVD) processes have been developed and evaluated in various
machining applications [1,2], e.g., for machining high-strength
Al-Si alloys and even aluminum matrix composites. Literature
has indicated that coating delamination is the major tool-life
limiting factor of diamond-coated inserts [3]. In general, tool
wear becomes rapid and can be catastrophic once delamination
is developed. Thus, an ability to detect coating failures is
necessary for process monitoring. Acoustic emission (AE)
signals have been applied for tool wear/fracture monitoring
because the frequency range of AE signals lies in a much
higher frequency domain [4], and both the intensity and
frequency responses have been investigated.
A survey of AE applications in tool wear monitoring for
machining can be found in a previous publication [5], from
which this study was extended. It has been established that AE
signals are dependent on the process parameters [6]. In
particular, a strong correlation of the AE root-mean-square
(RMS) voltages on both the strain rate and the cutting speed
was observed. On the other hand, one study explained the AE
signal characteristics related to various aspects from cuttings
such as materials, and concluded that tool wear is one of the
most influential factors contributing to an increase in the energy
of AE signals [7]. For example, tool fractures and catastrophic
failures may cause an unusual signal phenomenon, burst AE
signals [8], and. the power spectrum may exhibit a high
amplitude at a specific frequency range [9].
It has also been observed that an abrupt transition of the
AE magnitudes will occur with the progression of tool wear,
[10]. A separate study reported that AE sensors are very
sensitive to tool condition changes, with increased amplitude up
Copyright © 2010 by ASME
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to 160 kHz [11]. On the other hand, more recently, Feng et al.
analyzed the influence of tool wear on a microgrinding process
using acoustic emission, and the results shows that AE-RMS
(root-mean-square) signals are not monotonic with tool wear
sizes and thus may not be suitable for wear monitoring [12].
The other report claimed that AE-RMS signals are robust and a
versatile means of detecting the contact between the tool and
the part [13].
A few studies have been reported on AE signals for coated
tool wear monitoring. One observed that AE-RMS varied with
the coating type and did not necessarily increase with tool wear
[14]. Another reported that the AE-RMS values increased in
both amplitudes and fluctuations as the tool was about to reach
the end of its life [15]. Previous work conducted by the authors’
group applied an AE sensor to monitor diamond coating tool
failures in machining composites [5, 16]. It was noted that AE-
RMS time plots show notable evolutions in some failure cases,
however, it may not show clear delamination transition in other
cases. In addition, the frequency responses alter significantly
before and after coating failure. On the other hand, AE data
along cutting times generally show decreased intensity for low-
frequency peaks, but increased intensity for high-frequency
peaks. In addition, AE-FFT spectra of divided time periods
during one cutting pass may hint the coating failure transition.
Figure 1 below plots AE-FFT intensity changes along the
cutting time, both low and high frequencies, for two inserts
during the tool failure pass. For one insert (A), between the
period 2 and period 3, the low frequency peak decreases while
the high frequency peak increases. Similar results can be found
from another insert (B) between the period 2 and period 3.
However, the results are fairly qualitative for coating failure
monitoring and sometimes the difference may not be significant
enough to discriminate the transition, and thus, may result in
false judgments. It may become difficult to identify coating
failure by simply analyzing AE signals in the divided time
zones. A more definite method based on time increments may
be needed to detect the transition during the coating failure
pass. The objective of this study is to analyze the AE signal
evolutions, specifically the amplitude ratio of the high to low
frequency, using the STFT approach. It is anticipated that the
more intense analysis will yield quantitative information for
coating failure monitoring by AE signals.

0
500
1000
1500
2000
2500
3000
1 2 3 4
Time period
Amplitude
Low frequency
High frequency
(a) Insert A
0
200
400
600
800
1000
1200
1400
1600
1 2 3 4
Time period
Amplitude
Low frequency
High frequency
(b) Insert B
Figure 1. AE FFT magnitude comparisons in the four sub-
periods during the failure pass: (a) insert A and (b) insert B.
EXPERIMENT AND METHOD
The diamond-coated tools used had carbide substrates from
a tool supplier. The carbide substrates were made of fine-grain
WC with 6 wt.% cobalt. The substrate geometry is square-
shaped inserts (SPG422) that are 12.7 mm wide and 3.2 mm
thick with a 0.8 mm corner radius. For the coating process,
diamond films were deposited using a high-power microwave
plasma-assisted CVD process. The coating thickness at the rake
surface was about 15 µm, estimated from edge radius
measurements by an optical interferometer.
Outer diameter turning was performed in a computer
numerical control lathe to evaluate the wear progression of
diamond coated tools. The workpieces were round bars made of
A359/SiC-20p composite. The testing conditions used were 4
m/s cutting speed, 0.15 mm/rev feed, and 1 mm depth of cut
without cutting fluids. During machining testing, the cutting
insert was inspected, after each cutting pass, by optical
microscopy to examine if the coating failure occurs and the
flank wear-land width (VB) was measured. An AE sensor,
8152B Piezotron sensor from Kistler, was employed to acquire
data, both AE-RMS and AE-RAW (raw data), during the entire
machining operation. The signals were first fed into a coupler,
Kistler 5125B, for amplification and post-processing. The
resulting AE-RAW and AE-RMS were digitized at a 500 kHz
sampling rate per channel. In addition, MATLAB software was
used for data processing, such as FFT analysis for frequency
response.
The AE signals from different cutting pass were further
analyzed based on the STFT approach. Similar methods have
been applied in the event-related desynchronization [17,18].
Figure 2 shows the schematic of the STFT for one cutting pass.
The procedure of this method is as follows. First, the AE-Raw
data of this cutting pass is divided into several continuous
subset data (e.g. n). Each subset data has the same cutting time
interval (e.g., 2 s), and the previous subset data (e.g. subset 2)
can be 0.1 s earlier than the next subset data (i.e. subset 1),
where the 0.1 s is the time increment. Therefore, a total of n
subset data could be extracted from one cutting pass. Next,
each subset will be processed by FFT, and the amplitude
associated with the low frequency peak (~ 25 kHz) and high
frequency peak (~100 to 160 kHz) was analyzed and recorded
for each subset to compute the amplitude ratio of high/low

frequency. The amplitude ratio is then plotted along the cutting
time, which will be used to possibly capture the magnitude
transition for the high frequency component. The advantage of
this method is that the transition of the coating failure can be
tracked continuously to detect the event, which can be found by
the change of the amplitude ratio of high/low frequency. From
the previous results, the high frequency peak will increase
while the low frequency peak will decrease during or after the
coating failure pass. Thus, if the amplitude ratio of high/low
frequency shows dramatic increasing during a cutting pass, the
coating failure pass will be indentified and the transition could
be captured accordingly.
Figure 2. Illustration of STFT method.
RESULTS AND DISCUSSION
Figure 3 first displays the amplitude ratio of high/low
frequency with the STFT method for one previously tested
insert (Insert A) at different cutting passes: (a) initial cutting,
(2) prior to failure and (3) failure. It can be noted that the
change of amplitude ratios during the coating failure pass is
quite different from those at the other two cutting passes; a
clear increasing (value change over 1) was found during the
coating failure pass, while only some fluctuations were found
in the other two cutting passes (range of less than 0.5). The
result from the coating failure pass of another insert (Insert B),
Figure 4, however, is different from A, without noticeable
continued increasing. By further examinations of the tool wear
value after the coating failure pass, it was found the VB value
of insert B was smaller comparing to the insert A, 0.58 mm vs.
1.7 mm. Thus, it is possible that a threshold value may exist
below which the actual coating delamination or failure didn’t
occur during the final cutting pass. If further machining is
conducted using this insert, the amplitude ratio of high/low
frequency would reproduce the obvious increasing during the
next cutting pass or two.
(a) Initial cutting pass
(b) Prior to failure pass
(c) Failure pass
Figure 3. Amplitude ratio of high/low frequency by STFT
method during different passes for Insert A: (a) initial cutting
pass, (b) Prior to failure pass, and (c) Failure pass.
interval
incrementincrement
Subset 2
Subset 1
Subset n

(b) Insert B
Figure 4. Amplitude ratio of high/low frequency by STFT
method from Insert B during coating failure pass.
In order to further examine the above hypothesis, an
additional cutting experiment was conducted. Cutting
conditions were the same as the previous testing and the
acquisition and analysis of AE signals also followed exactly the
previous methodology. Tool wear was constantly examined
with machining forces monitored. Machining tests were
continued until the tool wear, VB, reached a high value to be
sure that delamination has occurred. Figure 5 shows tool wear
(VB) along cutting time for three different inserts including a
new test (Insert C); the cutting conditions were identical as the
previous tests [23] (Insert A and B). A noticeable variation is
observed as reported in the previous research. However, the
tools always showed a gradual increase of tool wear followed
by an abrupt increase of wear-land in one or two passes, during
which coating delamination occurred and resulted in rapid wear
of the exposed substrate material.
Figure 5. Tool wear development of cutting inserts (C, as well
as A and B) along cutting time.
Common methods, i.e., AE-RMS and AE-FFT analyses, to
investigate the AE signal behaviors at different cutting passes
were first used. Figure 6 shows AE-RMS vs. time from three
cutting passes (initial cutting, prior coating failure and coating
failure pass). AE-RMS decreased from ~2.5 V in initial cutting
to ~1.5 V in failure pass. However, there is no clear transition
during the failure pass that may be related to delamination.
Recall that for the other insert (A), it shows clear changes in
AE-RMS plot during the failure pass. The insert C and B, on
the other hand, do not show clear failure transition during the
failure cutting pass. Therefore AE-RMS alone from a cutting
pass may not be sufficient for coating failure identifications.
(b) Prior to coating failure pass
(c) Coating failure pass
Figure 6. AE-RMS of insert C: (a) initial cutting pass, (b)
second to coating failure pass, (c) coating failure pass.

Figure 7 displays AE-FFT spectra of the newly tested
insert (Insert C) at different cutting passes: (a) initial cutting,
(b) prior to coating failure, and (c) coating failure pass.
Comparing to the initial cutting pass, it can be seen that the AE-
FFT changes noticeably during the prior to failure pass and
coating failure pass, specifically, intensity reductions. This
result is similar to the previous work, however, with difference
in high frequency peaks, a much lower intensity [19]. It is also
found that the highest amplitude peak has changed from the
low frequency component around 25 kHz to the high frequency
component (100~160 kHz). A similar phenomenon, intensity
reductions, is also observed for the insert A and B. Thus, AE-
FFT is considered for monitoring coating failure conditions.
Figure 7. AE FFT of Insert C: (a) initial cutting pass, (b) prior
to coating failure pass, (c) coating failure pass.
Though AE-FFT provides some hints that may be related
to coating failure, it does not offer clear quantitative distinction
that can be used as a criterion. Thus, to examine whether
quantitative information of AE-FFT evolutions can be utilized
for coating failure detections, the AE raw signals from the
coating failure pass were further analyzed in details as before.
Specifically, the AE-RAW data was divided into 4 periods with
an equal cutting-time interval and FFT was further performed
to the AE subset data. Figure 8 compares AE-FFT spectra of
insert C at different cutting periods during the failure pass. The
intensity reduction for low frequency is very clear from the
period 3 to periods 4, while an obvious increase of the intensity
for high frequency is found from the period 2 to period 3, also
period 4. Figure 9 plots AE-FFT intensity changes along the
cutting time, low and high frequencies, for insert C during the
failure pass. The significant difference is between the period 3
and period 4, where the low frequency peak decreases while the
high frequency peak increases. The similar change was also
found for Insert A between the period 2 and period 3, but not in
the case of Insert B (Figure 1).
(a) First 25% cutting time

(b) Second 25% cutting time
(c) Third 25% cutting time
(d) Last 25% cutting time
Figure 8. AE-FFT at different time periods of Insert C during
the failure pass: (a) First, (b) Second, (c) Third and (d) Last
25% cutting of the entire pass.
Figure 9. AE FFT magnitude comparisons, insert C, during the
failure pass: (a) 25%, (b) 50%, (c) 75%, and (d) 100%.
In an attempt to capture the failure transition, the AE
signals from different cutting passes (of Insert C) were further
analyzed by the STFT method. Figure 10 plots the amplitude of
high/low frequency vs. cutting time at different cutting passes:
(a) initial cutting, (b) prior to coating failure pass and (c)
coating failure pass. It is undoubtedly noted that the change of
amplitude ratio of high/low frequency during the coating failure
pass is quite different, with a sharp increase (value change over
1.5), from those at the other cutting passes, which only exhibit
minor fluctuations along the cutting time (< 0.5). The results
are similar to that of Insert A during the coating failure pass.

Figure 10. Amplitude ratio of high/low frequency of Insert C:
(a) initial cutting pass, (b) prior to coating failure pass, (c)
coating failure pass.
Therefore, it may be referred, from the results above, that
if the flank wear of the insert exceeds a certain limit (e.g., 0.8
mm VB for testing in this study), the phenomenon of a sharp
increase in the amplitude ratio of high/low frequency will occur
during the coating failure pass. To further testify this
assumption and the method applied, further machining
experiments were conducted on machining composite with
other inserts with different cutting conditions. Figure 11 shows
the tool wear (VB) along cutting time from three different
inserts (D, E, F). Insert D and E had the same machining
parameters as the previous inserts but a different coating
thickness. The machining conditions used for insert F were 8
m/s cutting speed, 0.3 mm/rev feed, and 1 mm depth of cut. It
can be observed from the figure that the VB value at the end of
final cutting pass for the inserts all exceeded 0.8 mm.
Figure 11. Tool wear development of cutting inserts (D, E, F)
along cutting time.
Figure 12 displays the amplitude ratio of high/low
frequency obtained by the STFT method during the coating
failure pass of the insert D, E and F. The same phenomenon -
sharp increase in amplitude ratio - during the coating failure
pass was noted for all three inserts, except that Insert E exhibits
a decreasing then a notable increase. Therefore, the amplitude
ratio of the high frequency component (100 kHz to 160 kHz)
and the low frequency component (25 kHz) may be used to
monitor and capture coating failures by the STFT method.
(a) Insert D
(b) Insert E
(c) Insert F
Figure 12. Amplitude ratio of high/low frequency during the
coating failure pass: (a) Insert D, (b) Insert E, and (c) Insert F.
CONCLUSIONS
Following previous studies of AE signal analysis for
diamond coating failure monitoring in machining applications,
this research applied an STFT method to capture the coating

failure transition during cutting. The method uses sub-divided
signal segments, in a continuous manner, for the FFT analysis
and computes the amplitude ratio of high vs. low frequencies as
a function of cutting time during a cutting pass.
The results show that during the coating failure pass, a
clear sharp increase of amplitude ratio (value change over one)
of high/low frequency occurs along the cutting time. On the
other hand, the amplitude ratio only exhibits a rather low range
fluctuations in other passes, e.g., initial cutting and prior to
failure passes. Thus, it can be suggested that the applied STFT
method has a potential for diamond coating failure monitoring.
However, for coating failure associated with a smaller tool
wear (less than 0.8 mm VB), the amplitude ratio plot from the
STFT analysis may not clearly identify the failure transition.
ACKNOWLEDGMENTS
This material is based upon work supported by the
National Science Foundation under Grant No. CMMI 0728228.
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