msec2010: short-time fourier transform method in ae signal analysis for diamond coated failure...

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

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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.

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(a) Insert A

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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 increment increment

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.

REFERENCES 1. Castro, G., Almeida F.A., Oliveira F.J., Fernandes A.J.S.,

Sacramento J., and Silva R.F., (2008), “Dry machining of silicon–aluminium alloys with CVD diamond brazed and directly coated Si3N4 ceramic tools,” Vacuum, 82(12) ,pp.1407-1410.

2. Qin, F., Hu, J., Chou, Y.K., and Thompson R.G., (2009), “Delamination wear of nano-diamond coated cutting tools in composite machining,” Wear, 267(5-8),pp. 991-995.

3. Hu, J., Chou, Y.K., Thompson, R.G., Burgess, J., and Street, S., (2008), “Nanocrystalline Diamond Coating Tools for Machining High-strength Al alloys,” International Journal of Refractory Metals and Hard Materials, 26, pp. 135-144.

4. Dornfeld, D., (1992), “Application of acoustic emission techniques in manufacturing,” NDT & E International, 25(6), pp.259-269.

5. Lu, P., Chou, Y. K., and Thompson, R. G., (2009), “AE single evolution in machining by diamond coated tools,” Proceedings of the ASME 2009 International Manufacturing Science and Engineering Conference, October 4-7,2009, West Lafayette, Indiana, MSEC2009-84372.

6. Dornfeld, D.A., and Kannatey, A.E., (1980), “Acoustic emission during orthogonal metal cutting,” International Journal of Mechanical Sciences, 22, pp. 285-296.

7. S. Dolinsek and J. Kopač, (1999), “Acoustic emission signals for tool wear identification,” Wear, 225–229, pp. 295–303.

8. Dornfeld, D.A., and Lan M.S., (1983), “Chip form detection using acoustic emission,” Proceedings of 13th North American Manufacturing Research Conference. pp. 386-389.

9. Lan, M.S., and Dornfeld, D.A., (1982), “Experimental studies of tool wear via acoustic emission analysis,”

Proceedings of the 10th North American Manufacturing Research Conference, pp. 305-311.

10. Mukhopadhyay, C.K., Venugopal, S., Jayakumar, T., Nagarajan, R., Mannan, S.L., and Raj, B., (2006), “Acoustic emission monitoring during turning of metal matrix composite and tool wear,” Materials Evaluation, 64(3), pp. 323-330.

11. Haber, R.E., Jiménez, J.E., Peres, C.R. and Alique, J.R., (2004), “An investigation of tool wear monitoring in a high-speed machining process,” Sensors and Actuators A: Physical, 116, pp. 539–545.

12. Jemielniak, K., Arrazola, P.J.,(2008), “Application of AE and cutting force signals in tool condition monitoring in micro-milling,” CIRP Journal of Manufacturing Science and Technology, 1(2), pp. 97-102.

13. Feng, J., Kim, B.S., Shih, A., Ni, J. (2009), “Tool wear monitoring for micro-end grinding of ceramic materials,” Journal of Materials Processing Technology, 209(11), pp. 5110-5116.

14. Somasundaram, S., and Raman, S., (1993), “Acoustic emission studies while machining with coated tools,” Transactions of NAMRI/SWE, XXI, pp. 83-94.

15. Moriwaki, T., and Tobito, M., (1990), “A new approach to automatic detection of life of coated tool based on acoustic emission measurement,” ASME Journal of Engineering for Industry, 112, pp. 212-218.

16. Hu, J., Qin, F., Y. K. Chou, and R. G. Thompson, (2008), "Characteristics of Acoustic Emission Signals in Machining Using Diamond Coated Cutting Tools,” Proceedings of the 2008 International Manufacturing Science and Engineering Conference, October 7-10, Evanston, Illinois, 2008, MSEC2008-72507.

17. Guilleminault, C., Black, J., Carrillo O. (1997), “EEG arousal and Upper Airway Resistance Syndrome”, Electroencephalography and Clinical Neurophysiology, 103, pp. 11.

18. Sutoh, T., Yabe, H, Shinozaki, N., Hiruma, T., Sato, Y., Nashida, T., Kaneko, S. (1997), “‘Gabor filter’ technique for analyzing event-related desynchronization,” Electroencephalography and Clinical Neurophysiology, 103 (1), pp. 154.
