Diamond coated cutting tools have a potential to replace costly polycrystalline diamond tools. However, coating delaminations remain the primary wear mode that often results in catastrophic tool failures, causing to poor part quality and possible damage to machine tools. Moreover, delamination events are difficult to be precisely predicted. Thus, tool delamination identification is necessary for process monitoring. Following a previous work, this study examines the acoustic emission (AE) signal evolutions during machining by diamond coated tools, in particular, the frequency response along cutting time as well as during a specific cutting pass. The intent was to correlate the characteristics of the AE spectral components with coating delaminations. The results are summarized as follows. Though AE root-mean-square values have been used to monitor tool failure, it may not show clear transition registered to coating delamination in some cases. The fast Fourier transformation (FFT) spectra of AE data along cutting time generally show decreased intensity for low frequency peaks, but increased intensity for high frequency peaks. In addition, the AE FFT spectra of sub-divided time zones during one cutting pass may clearly indicate the coating failure transition.

1. 1 Copyright © 2009 by ASME
Proceedings of the ASME 2009 International Manufacturing Science and Engineering Conference
MSEC2009
October 4-7, 2009, West Lafayette, Indiana, USA
MSEC2009-84372
AE SIGNAL EVOLUTIONS IN MACHINING BY DIAMOND COATED TOOLS
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
Diamond coated cutting tools have a potential to replace
costly polycrystalline diamond tools. However, coating
delaminations remain the primary wear mode that often results
in catastrophic tool failures, causing to poor part quality and
possible damage to machine tools. Moreover, delamination
events are difficult to be precisely predicted. Thus, tool
delamination identification is necessary for process monitoring.
Following a previous work, this study examines the
acoustic emission (AE) signal evolutions during machining by
diamond coated tools, in particular, the frequency response
along cutting time as well as during a specific cutting pass. The
intent was to correlate the characteristics of the AE spectral
components with coating delaminations. The results are
summarized as follows. Though AE root-mean-square values
have been used to monitor tool failure, it may not show clear
transition registered to coating delamination in some cases. The
fast Fourier transformation (FFT) spectra of AE data along
cutting time generally show decreased intensity for low
frequency peaks, but increased intensity for high frequency
peaks. In addition, the AE FFT spectra of sub-divided time
zones during one cutting pass may clearly indicate the coating
failure transition.
INTRODUCTION
Synthetic polycrystalline diamond (PCD) is commonly
used in the industry to machine non-ferrous materials because
of its exceptional tribological properties. However, processing
and fabrications of PCD tools are of high cost. On the other
hand, 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 aluminum matrix composites. Several previous
experimental investigations have proved coating delamination
is the dominant mechanism that dictates the life of diamond
coated cutting tools [3]. Once delamination occurred, tool wear
is rapid, and could be catastrophic, causing part rejections,
possible damage to the machine tool. Moreover, delamination
event is difficult to predict because of the process complexity.
Thus, it is of great interest for tool users to be able to detect or
warn in advance coating delamination as a process monitoring
means. Among common sensors deployed in machining
operations, acoustic emission (AE) sensors have been evaluated
for tool wear monitoring.
Dornfeld’s group at UC-Berkeley is perhaps the pioneer to
study AE signals in cutting and to explore AE for machining
process monitoring. Dornfeld and Kannatey performed
orthogonal cutting tests, varied the process parameters and
recorded the acoustic emission signals generated [4]. The
authors indicated that strong dependence of the AE root-mean-
square (RMS) voltages on both the strain rate and the cutting
speed was observed. Moreover, Dornfeld and Lan reported that
tool fracture and catastrophic failure generate burst AE signals
[5]. In a separate study, Lan and Dornfeld reported that the AE
power spectrum exhibits high amplitude at a specific frequency
range during the tool fracture [6].
Since then, AE signals associated with machining have
been frequently studied. In studying AE signals during
discontinuous chip formations in machining, Mukhopadhyay,
and Venugopal have also found that AE magnitudes will
Proceedings of the ASME 2009 International Manufacturing Science and Engineering Conference
MSEC2009
October 4-7, 2009, West Lafayette, Indiana, USA
MSEC2009-84372
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2. 2 Copyright © 2009 by ASME
increase abruptly once a certain amount of tool wear is reached
[7]. Emel and Kannatey-Asibu applied AE and force sensor
fusion to monitor cutting processes and reported that using only
AE for tool breakage reached a 96% accuracy [8]. Teti, on the
other hand, indicated a drawback of AE technique for tool wear
monitoring, i.e., dependence upon the cutting conditions [9].
Dolinsek and J. Kopač attempted to explain the AE signal
contents related to various aspects from cuttings such as
materials. The authors reported that tool wear is one of the most
influential factors contributing to an increase in the energy of
AE signal [10]. Li et al. combined the AE frequency response
and fuzzy logic neural network to attempt monitoring of
drilling processes [11]. Their experimental results showed that
the frequency distribution of AE signals changes as the tool
wears. One of the authors of that study, Li, presented a review
of AE methods for tool wear monitoring [12]. The author
reported different methodologies of AE signal processing
including fast Fourier transformation (FFT) and wavelet
transform. The author also noted that AE signals are sensitive
to tool wear and fracture, however, they are also highly
dependent upon process parameters.
Haber et al. reported tool wear monitoring in high-speed
machining using various sensor techniques including AE [13].
The authors pointed out that, from spectral analysis of AE
signals, AE sensors are very sensitive to tool conditioning
changes, with increasing amplitude up to 160 kHz. More
recently, Jemielniak and Arrazola applied AE sensors for tool
condition monitoring in micro-milling and reported that AE
signals still show dependence on tool wear in micro-scale
cutting [14]. Kanga et al. also applied AE for tool condition
monitoring in small-scale part machining [15]. The authors
claimed that AE RMS values can be used for tool monitoring.
Investigations of AE signals for coated tool wear
monitoring are scarce. Somasundaram and Raman conducted
machining tests with different types of coated tools and noted
that AE RMS varied with the coating type and did not
necessarily increase with tool flank wear [16]. Moriwaki and
Tobito reported that there was a transition from the burst-type
to the continuous AE RMS amplitude with the progression of
tool wear. In addition, the RMS values increased in both
amplitude and fluctuation as the tool was about to reach the end
of its life [17]. Cho and Komvopoulos performed the frequency
analysis of the AE signals collected when machining by coated
tools [18]. Their results indicated that coating delamination
may produce high-frequency burst-type signals.
A preliminary work [19] conducted by the authors’ group
applied an AE sensor to monitor diamond coating failure in
machining. It was found that AE-RMS may show possibly
noticeable reductions between before and after coating failure.
Moreover, the frequency response may alter significantly
before and after coating failure. In addition, burst signals of
AE-RAW data were also noted, but not consistently. However,
the results do not specify the failure transition period. It is
possible that diamond coating is brittle and failure may occur
during the entry or exit during the cut, which may become
difficult to identify by simply analyzing AE signals at different
cutting passes.
The primary objective of this study is to examine the AE
signal evolutions in details, specifically the AE-FFT spectra
along cutting time, also at different time periods during one
cutting pass. The intent was to correlate the characteristics of
the AE spectral components with coating delaminations.
EXPERIMENTAL SET-UP
The substrates used for diamond coating experiments,
square-shaped inserts (SPG422), were fine-grain WC with 6
wt.% cobalt. For the coating process, diamond films were
deposited using a high-power microwave plasma-assisted CVD
process. A gas mixture of methane in hydrogen was used as the
feedstock gas. Nitrogen, maintained at a certain ratio to
methane, was inserted to the gas mixture to obtain
nanostructures by preventing cellular growth. The pressure was
about 30 to 55 Torr and the substrate temperature was about
685 to 830 °C. The coating thickness was about 15 µm,
estimated from edge radius measurements by an interferometer.
A computer numerical control lathe, Hardinge Cobra 42,
was used to perform machining experiments, outer diameter
turning, to evaluate the tool wear of diamond coated tools. With
the tool holder used, the diamond coated cutting inserts formed
a 0o
rake angle, 11o
relief angle, and 75o
lead angle. The
workpieces were round bars made of A359/SiC-20p composite.
The machining conditions used were 4 m/s cutting speed, 0.15
mm/rev feed, 1 mm depth of cut and no coolant was applied.
For each coating thickness, two tests were repeated. During
machining testing, the cutting inserts were inspected, after each
cutting pass, by optical microscopy to examine if the coating
failure occurs. Worn tools after testing were also observed by
scanning electron microscopy (SEM) to confirm coating
failure. In addition, cutting forces were monitored during
machining using a Kistler dynamometer.
An AE sensor was employed to acquire data, both AE-
RMS (root-mean-square value) and AE-RAW (raw data),
during the entire machining operation. The sensor used was
8152B Piezotron acoustic emission sensor from Kistler, and the
signal was first fed into a coupler, Kistler 5125B, for
amplification and post-processing. The time constant for the
RMS values was 1.2 ms. The resulting AE-RAW and AE-RMS
were digitized using an IOtech high-speed DAQ3000 PCI
board at 500 kHz sampling rate per channel. In addition,
MATLAB software was used to evaluate the frequency
response, using FFT analysis, of the AE raw data at different
cutting time.
RESULTS AND DISCUSSION
Figure 1 first shows tool wear, flank wear-land width
(VB), along cutting time from two tests (using 2 inserts) under
identical cutting conditions. A noticeable variation is observed.
In general, the tools showed a gradual increase of tool wear
followed by an abrupt increase of wear-land in one or two
passes. It is believed that, during those specific passes, coating
delamination occurred and resulted in rapid wear of the
exposed substrate material.
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3. 3 Copyright © 2009 by ASME
0
0.2
0.4
0.6
0.8
0 3 6 9 12
VB(mm)
Cutting Time (min)
A B
Figure 1. Tool wear development, of 2 cutting inserts (A and
B), along cutting time.
Figure 2 shows AE-RMS evolution during the coating
failure pass, confirmed by tool observations in an optical
microscope, from two identical cutting tests (2 cutting inserts,
A and B). It is noted that one of them has very clear failure
transition (Insert A, Figure 2a), while the other one does not
(Insert B, Figure 2b). Therefore, AE-RMS alone from a cutting
pass may not be sufficient for coating failure identifications.
Time (s)
AE‐RMS(V)
(a) With clear failure transition
Time (s)
AE‐RMS(V)
(b) Without clear failure transition
Figure 2. AE-RMS during tool failure pass: (a) with clear
failure transition period (insert A), (b) without clear failure time
(inert B).
Figure 3 displays AE-FFT spectra of insert A at different
cutting passes: (a) initial cutting and (b) coating failure pass. It
can be seen that the AE-FFT changes noticeably between the
two passes, specifically, intensity reductions. This result is
similar to the previous work, however, with difference in high
frequency peak, much lower intensity [19]. On the other hand,
Figure 4 displays AE-FFT spectra of insert B at different
cutting passes: (a) initial cutting and (b) coating failure pass. A
similar phenomenon, intensity reductions, is observed. Note
that, from Figure 2, this insert did not show clear failure
transition in AE-RMS results during the failure pass. Therefore,
it may be possible to use AE-FFT evolutions to monitor the
coating failure conditions.
Frequency (kHz)
Amplitude
(a) Initial cutting pass
Frequency (kHz)
Amplitude
(b) Coating failure pass
Figure 3. AE FFT of Insert A: (a) initial cutting pass, (b)
coating failure pass.
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4. 4 Copyright © 2009 by ASME
Frequency (kHz)
Amplitude
(a) Initial cutting pass
Frequency (kHz)
Amplitude
(b) Coating failure pass
Figure 4. AE FFT of Insert B: (a) initial cutting pass, (b)
coating failure pass.
To examine if quantitative information of AE-FFT
evolutions can be utilized for coating failure identifications,
The amplitude associated with the low frequency peak (~ 25
kHz) and high frequency peak (~125 to 150 kHz) were
analyzed and recorded for each cutting pass. Figure 5 plots AE-
FFT magnitude changes along the cutting time: low and high
frequencies for insert A (clear failure transition). On the other
hand, Figure 6 plots AE-FFT magnitude changes along the
cutting time for insert B (unclear failure transition), also low
and high frequencies. A general trend of intensity reductions
along cutting time coincides with the observations from Figures
3 and 4. However, the fluctuation of peak intensity poses
difficulty in specifying certain threshold values for coating
failure identifications.
0
1000
2000
3000
4000
5000
6000
0 100 200 300 400
Cutting Time(s)
Amplitude
Low frequency
High frequency
Figure 5. AE-FFT intensity (high and low frequencies)
evolutions along cutting time, insert A.
0
1000
2000
3000
4000
5000
6000
7000
0 200 400 600 800
Cutting Time(s)
Amplitude
Low frequency
High frequency
Figure 6 AE-FFT intensity (high and low frequencies)
evolutions along cutting time, insert B.
The AE signals in the coating failure pass were further
analyzed in details, specifically, the AE-RAW data was divided
into 4 equal cutting periods and FFT was further performed for
the AE subset data. Figure 7 compares AE-FFT spectra of
insert A at different cutting periods during the failure pass. The
intensity reduction is very clear from period 2 to periods 3 and
4. Figure 8 plots AE-FFT intensity changes along the cutting
time, low and high frequencies, for insert A. The significant
difference is between the period 2 and period 3, where the low
frequency peak decreases while the high frequency peak
increases. This result is consistent with the failure transition
period observed in Figure 2a.
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5. 5 Copyright © 2009 by ASME
Frequency (kHz)
Amplitude
(a) First 25% cutting time
Frequency (kHz)
Amplitude
(b) Second 25% cutting time
Frequency (kHz)
Amplitude
(c) Third 25% cutting time
Frequency (kHz)
Amplitude
(d) Last 25% cutting time
Figure 7. AE-FFT at different time periods of Insert A during
the failure pass: (a) First, (b) Second, (c) Third, and (d) Last
25% cutting of the entire pass.
0
500
1000
1500
2000
2500
3000
1 2 3 4
Time period
Amplitude
Low frequency
High frequency
Figure 8. AE FFT magnitude comparisons, insert A, during the
failure pass: (a) 25%, (b) 50%, (c) 75%, and (d) 100%.
On the other hand, Figure 9 compares AE-FFT spectra of
insert B at different cutting periods during the failure pass.
Different from inset A, the AE-FFT spectra of all 4 periods
have similar characteristics, no noticeable intensity changes.
Figure 10 further plots AE-FFT magnitude changes along the
cutting time: low and high frequencies for insert B (unclear
failure transition). The trend of amplitude changes is marginal,
but, not as clear as in the insert A plot (Figure 7). Moreover, the
AE-RMS cannot confirm if the failure occurs during this pass
as shown in Figure 2b. Figure 11 shows the AE-FFT spectrum
from the prior failure pass, last 25% time period, of insert B.
Comparing Figures 11 and 9a, it is reasonable to argue that
coating failure (delamination ) might possibly occur at close to
the end of prior failure pass or in the very beginning of the
failure pass. Therefore, the AE-RMS and AE-FFT of the failure
pass do not reflect the coating delamination event. The
implication of such results is that global analyses of AE signals
(i.e., of different passes) may not be sufficient for coating
failure identifications. Instead, local analyses (i.e., divided
periods in a cutting pass, across different passes) may be
needed to capture coating failure events.
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6. 6 Copyright © 2009 by ASME
Frequency (kHz)
Amplitude
(a) First 25% cutting time
Frequency (kHz)
Amplitude
(b) Second 25% cutting time
Frequency (kHz)
Amplitude
(c) Third 25% cutting time
Frequency (kHz)
Amplitude
(d) Last 25% cutting time
Figure 9. AE-FFT at different time periods of Insert B during
the failure pass: (a) First, (b) Second, (c) Third, and (d) Last
25% cutting of the entire pass.
0
200
400
600
800
1000
1200
1400
1600
1 2 3 4
Time period
Amplitude
Low frequency
High frequency
Figure 10. AE FFT magnitude comparisons, insert B, during
the failure pass: (a) 25%, (b) 50%, (c) 75%, and (d) 100%.
Frequency (kHz)
Amplitude
Figure 11. AE-FFT from the last 25% time period in the prior
failure pass of Insert B.
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7. 7 Copyright © 2009 by ASME
CONCLUSIONS
In this study, cutting experiments were designed and
conducted to investigate acoustic emission (AE) signals
generated during machining A359/SiC/20p composite using
diamond coated cutting tools. AE signals were collected and
analyzed in detail, in particular, the frequency response along
the cutting time as well as during a cutting pass. The intent was
to correlate the characteristics of the AE spectral components
with coating delaminations. The results are summarized as
follows.
(1) Though AE root-mean-square values have been used to
monitor tool failure, it may not show clear transition
registered to coating delamination in some cases.
(2) The fast Fourier transformation (FFT) spectra of AE data
along cutting time generally show decreased intensity for
low frequency peaks, but increased intensity for high
frequency peaks.
(3) In addition, the AE FFT spectra of sub-divided time zones
during one cutting pass may clearly indicate the coating
failure transition.
ACKNOWLEDGMENTS
This material is based upon work supported by the
National Science Foundation under Grant No. CMMI 0728228.
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