Journal of Materials Processing Technology 109 (2001) 242±247
Some aspects of acoustic emission signal pre-processing Krzysztof Jemielniak* Warsaw University of Technology, Institute of Manufacturing Engineering, Narbutta 86, 02-524 Warsaw, Poland
Abstract The acoustic emission (AE) sensor and the pre-ampli®er either built-in or connected to the sensor are the key elements in any AE based tool condition monitoring (TCM) system. This paper provides an interpretation of some common AE signal distortions and possible solutions to avoid such problems. The ®rst two are AE signal saturation and temporary vanishing of the signal amplitude caused by overload of the pre-ampli®er. The other is a result of multiple re¯ections of the AE wave on the different surfaces through the signal's path. # 2001 Elsevier Science B.V. All rights reserved. Keywords: Acoustic emission; Signal pre-processing
1. Introduction When working with any acoustic emission (AE) based tool condition monitoring (TCM) system, three basic elements should be considered, regarding their compatibility: the AE signal itself, a sensor and a pre-ampli®er. Reliability of the AE system relies heavily on how these elements match each other. Many of the AE sensors with their built-in preampli®ers, which are originally designed for the `traditional' applications of non-destructive material testing, may not be suitable for use in metal cutting, as the signals originating from the cutting zone can be considerably strong. Because of the characteristics of pre-processing units, such high amplitude signals sometimes cause overloading of the pre-ampli®er and distortion of the signal. This can often result in a misleading evaluation of the data. Fig. 1 presents examples of distorted AE signals received from two different sensors and pre-processing units. The ®rst one was typical laboratory transducer, and saturation of the signal (left ®gure) attests a simple overload of the ampli®er. Moreover in both the signals another less obvious distortion can be seen Ð the AE signal temporarily vanishes. Discussion of these AE signal distortion and their possible causes is the main goal of this paper. As raw AE signal (AEraw) has a very high frequency, its recording and analysis in the original form is expensive. Therefore, usually root mean square value (AERMS) or some other form of demodulation is applied. Signals recti®ed in this way have much lower frequency, so they are much easier * Tel.: 48-22-6608656; fax: 48-22-8490-285. E-mail address:
[email protected] (K. Jemielniak).
to handle, being in most cases still useful enough. However, that kind of signal processing should be carried out with care, especially as far as integration time constant is concerned. Furthermore, if the raw signal has been deformed, this deformation can be completely concealed by demodulation and leads to deceptive conclusions. Some aspects of AE signal demodulation will be discussed here as well. 2. The measuring chain The typical procedure of processing of the AE signal in metal cutting follows the pattern schematically illustrated in Fig. 2. The piezoelectric AE sensor is usually placed as close as possible to the cutting zone, e.g. to the tool shank, the tool post, the head stock or to the spindle. Because of high impedance of the sensor it must be directly connected to a buffer ampli®er. Low frequency noise components, which are inevitably present in AE signal, are considered to be not correlated with tool's condition and hence useless. Besides, they can be of high amplitude forcing usage of lower signal ampli®cation. This results in lower ampli®cation of useful band of the signal. Therefore, those components should be eliminated (high-pass ®ltered) at the earliest possible stage of signal processing to enable usage of full amplitude range of the equipment. Sometimes, the AE signal is then fed through a low-pass ®lter to get rid of the high frequency noise components due to electric sparks, etc. or to avoid aliasing. The ®ltered AE signal is a subject to further processing and/or recording. The raw AE signal can be demodulated in the form of the mean value or RMS to obtain a low frequency variable, so it can be recorded or
0924-0136/01/$ ± see front matter # 2001 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 4 - 0 1 3 6 ( 0 0 ) 0 0 8 0 5 - 0
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Fig. 1. Examples of distorted AE signals: (a) obtained from BruÈel & Kjñr 8313 sensor with 2637 pre-ampli®er equipped with 200 kHz octave bandwidth ®lter; (b) obtained from Kistler 8152A1 sensor with a Kistler piezotron coupler 5125A with HPS 50 kHz and LPF 1 MHz.
Fig. 2. A typical measuring chain in an AE measurement system in metal cutting.
processed with the conventional, less expensive signal processing equipment. Fig. 3 presents AEraw signal obtained from broad band transducer BruÈel & Kjñr 8312 without ®ltering, when cutting carbon steel 45 with a CSRNR 2525 tool with conventional carbide insert SNUN S30S, at vc 180 m=min,
ap 2:5 mm, f 0:33 mm=rev and KT 0:25 mm. Long view (Fig. 3a) shows three bursts of nearly alike maximum amplitude. In demodulated signal (AERMS, Fig. 3b) the third burst reaches higher value. Under both ®gures two fragments are marked with black rectangulars for closer examination. They are shown in Fig. 3c, and their amplitude spectra are
Fig. 3. AE signal obtained from BruÈel & Kjñr 8312 transducer: a long view (a), RMS value of the signal (b), selected fragments of the signal (c) and their spectra (d).
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Fig. 4. AE signal from Fig. 3 after ®ltering (HPF 150 kHz, LPF 500 kHz): a long view (a), RMS value of the signal (b), selected fragments of the signal (c) and their spectra (d).
presented in Fig. 3d. The ®gures reveal existence of dominant low frequency component. It was caused by some mechanical disturbances, consequently should be ®ltered out as irrelevant to tool wear. Of course in AERMS signal, low and high frequency components cannot be distinguished, making necessity for the ®ltering even more imperative. Fig. 4 presents results of high-pass (150 kHz) and lowpass (500 kHz) ®ltering of the signal from Fig. 3. Only two of the three bursts visible in Fig. 3 remained here, and the second is much lower than the ®rst one. The third, biggest burst visible in Fig. 3 just disappeared. Now amplitude spectra consist of components only from interesting frequency range. Ef®cient processing of AE signal relies heavily on the characteristics of the pre-amplifying units, which are usually designed to work with a speci®c sensor (transducer). The BruÈel & Kjñr pre-ampli®er type 2637, e.g. is primarily designed to work with the BruÈel & Kjñr transducers type 8313 and 8314. The pre-ampli®er is equipped with interchangeable plug-in ®lters (200, 800 kHz octave-band or ``linear'') [1]. Another example is the Kistler AE-Piezotron Coupler 5125A with a built-in RMS converter. This has been designed for processing AE signals from Kistler sensors type 8152A1 and 8152A2. The ampli®er has two seriesconnected ®lters of second order, designed as plug-in elements. The standard cut-off frequency are: low-pass ®lter 1000 kHz and high-pass ®lter 50 kHz. The standard integration time constant of the RMS converter is 1.2 ms, but 0.12, 12 and 120 ms are also available [4,5].
and b). These parts of the signal in which low frequency component was dominant after quadruple ampli®cation became rectangular. Let us assume now that this distorted signal has been fed through the same ®lters as described above (LPF 500 kHz and HPF 150 kHz). Resulting signal presented in Fig. 5c and e appears to be similar to that shown in Fig. 1a. Therefore, one can conclude that signal distortion presented in Fig. 1 was caused by the same reasons as described here. Temporal vanishing of the AE signal reveals to be a result of ®ltering of saturated, rectangular signal of low frequency major component. Not only did the amplitude spectra (shown in Fig. 5f) have shape different from the proper ones (shown in Fig. 4d), but their values are merely twice instead of four times higher. And ®nally any signal distortion cannot be noticed in AERMS signal (Fig. 5d), which also reaches no more than a half of the correct value, and its shape barely resembles the actual one (Fig. 4b). It should be pointed out that such signals must be considered as completely distorted, thus useless. To avoid such problems the gain of the buffer ampli®er should be as small as possible, and any further necessary ampli®cation should be done after signal ®ltering. In other words, the AE signals should be high-pass ®ltered at the earliest possible stage of processing, just after the unavoidable buffering. It is particularly important while using AERMS signals only instead of AEraw. To avoid the signal distortion presented in Fig. 1a, the BruÈel & Kjñr pre-ampli®er type 2637 used in WUT was modi®ed by lowering its gain by 10 (20 dB).
3. Possible causes of signal distortion
4. Shape and duration of AE bursts
To ®nd out possible causes of the AE signal distortion shown in Fig. 1, let us consider what would have happened if the signal discussed in Figs. 3 and 4 had been four times stronger. At ®rst, it would overload the buffer ampli®er resulting in characteristical signal saturation (see Fig. 5a
The AE transducers can be calibrated by a method after Nielsen±Hsu [4]. It is based on breaking of a pencil lead on a steel plate to generate an AE signal, which is detected by the transducer as surface (Rayleigh) waves. As the duration of the signal generated by the graphite lead breakage is very
K. Jemielniak / Journal of Materials Processing Technology 109 (2001) 242±247
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Fig. 5. AE signal from Fig. 3 after quadruple ampli®cation (a and b), and ®ltering: a long view (c), RMS value of the signal (d), selected fragments of the signal (e) and their spectra (f).
short, its spectrum can be approximately considered as rectangular. Ergo, the frequency response of the received signal waveform can be assumed as the amplitude characteristics of the sensor (and signal processing circuit). The sensor calibration set-up is shown in Fig. 6 and exemplary characteristics obtained this way are presented in Fig. 7 [3]. The method can also be applied to analysis of the AE signal dependence on its path. Acoustic emission undergoes every rule concerning wave propagation and is considerably attenuated and signi®cantly transformed when it ®nally reaches the sensor [2]. Fig. 8 presents a comparison of the three AE signal waveforms obtained from BruÈel & Kjñr 8313 sensor with modi®ed pre-ampli®er (third characteristic in Fig. 7). The ®rst (Fig. 8a) is a result of Nielsen±Hsu test. The second (Fig. 8b) presents AE waveform obtained when
graphite was broken on the tool face (CSRNR 2525-12 with indexable carbide insert SNUN 120408 S30S) and sensor was ®xed on the upper surface of the tool post. The third (Fig. 8c) is a burst obtained in real cutting with the same tool, so signal path in the second and the third case was approximately the same. It has been already said that breakage of the graphite lead excites very short impulse of AE. Waveform presented in Fig. 8a is the time response of the sensor on this excitation. Duration of the signal is much longer than the exciting impulse, because AE sensors work in their resonance frequency range. When the lead was broken on the tool ®xed in the tool post, not only is the signal attenuated (lower RMS value for the same time) but the same excitement resulted in quite different waveform. Amplitude of the signal did not rise sharply like in Nielsen±Hsu test and
Fig. 6. Sensor calibration set-up.
Fig. 7. Characteristics of some AE sensors.
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Fig. 8. Comparison of AE bursts registered in Nielsen±Hsu test (a), after braking the lead on the tool face (b) and in real cutting (c).
Fig. 9. AE signals obtained from Kistler 8152A1 sensor with a piezotron coupler 5125A during interrupted cutting; workpiece: carbon steel 45, tool: CSRNR 2525 SNUN 120408 NT 35 (TiC TiN coated sintered carbide).
K. Jemielniak / Journal of Materials Processing Technology 109 (2001) 242±247
reached much lower maximum level, however its decrease lasted much longer. Consequently, resulting overall burst duration was some 10 times longer. This can be attributed to multiple re¯ections of the wave on the different surfaces through the signal's path. Signal in Fig. 7b lasts more or less as long as typical burst shown in Fig. 8c. Therefore, one can conclude that bursts recorded by sensor positioned in some distance from the cutting zone are quite different from actual AE bursts regarding the shape and duration. In particular, it can be taken for granted that they last much longer than real ones. Duration of registered AE burst can also be severely affected by an improper integration constant of the RMS converter which effects in misleading results. Webster et al. [6] investigated AE signals of grinding recommended an integration constant of around 1 ms for such applications. Fig. 9 presents comparison of AEraw and AERMS obtained from Kistler 8152A1 sensor and its pre-ampli®er while interrupted turning. Standard integration constant 1.2 ms was applied here. Both examples have been registered during tool engagement after the break. First burst shown in Fig. 9a lasts for some 2 ms which is approximately equal to AERMS rise time (double integration constant), therefore burst duration observed by AERMS is about 10 times longer than the actual one. This can result in burst overlapping as shown in Fig. 9b, where series of bursts have been transformed into one in AERMS signal, making it useless for, e.g., burst counting. For such purposes integration time constant around 10 times shorter should be applied.
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5. Conclusions 1. The AE buffer ampli®er gain should be as small as possible to avoid pre-ampli®er's overloading and signal's distortion. Any further necessary signal ampli®cation should be carried out after high-pass ®ltering. 2. In machine tool's environment the AE signal is multiply re¯ected from inner surfaces of a structure which results in prolonged duration of the signal received by the sensor. Actual duration of AE bursts is much shorter. 3. If AERMS signal is to be used for AE bursts analysis in cutting, integration time constant should not exceed about 0.1 ms. References [1] BruÈel & Kjñr, Acoustic Emission Transducers and Preampli®ers, 1984. [2] K. Iwata, T. Moriwaki, An application of acoustic emission measurement to in-process sensing of tool wear, Ann. CIRP 25 (1) (1977) 21±26. [3] K. Jemielniak, O. Belgassim, Characteristics of acoustic emission sensors employed for tool condition monitoring, in: Proceedings of the Seventh Workshop on Supervising and Diagnostics of Machining Systems, Technical University of Wroclaw, CIRP, Karpacz, 1996, pp. 241±252. [4] Kistler, Instrumentation Corp., Piezotron Acoustic Emission Sensor 8125A, Piezo-Instrumentation, No. 8.8152A, 1994. [5] Kistler, Instrumentation Corp., AE Piezotron Coupler 5125A, PiezoInstrumentation, No. 12.5125, 1995. [6] J. Webster, W.P. Dong, R. Lindsay, Raw acoustic emission signal analysis of grinding process, Ann. CIRP 45 (1) (1996) 335±340.
