c Allerton Press, Inc., 2011. ISSN 8756-6990, Optoelectronics, Instrumentation and Data Processing, 2011, Vol. 47, No. 1, pp. 93–103. c C.V. Panin, A.V. Byakov, V.V. Grenke, I.V. Shakirov, O.V. Bashkov, 2011, published in Avtometriya, 2011, Original Russian Text Vol. 47, No. 1, pp. 115–128.
COMPUTATIONAL AND INFORMATION-MEASURING SYSTEMS
Developing and Testing a Laboratory System for Recording and Analysis of Acoustic Emission C. V. Panina , A. V. Byakova , V. V. Grenkea , I. V. Shakirova , and O. V. Bashkovb a
Institute of Strength Physics and Materials Science, Siberian Branch, Russian Academy of Sciences, pr. Akademicheskii 2/4, Tomsk, 634021 Russia E-mail:
[email protected] b Komsomolsk-on-Amur State Technical University, pr. Lenina 27, Komsomolsk-on-Amur, 681013 Russia Received June 16, 2010
Abstract—A laboratory system for recording and analyzing acoustic emission was designed and experimentally investigated (tested). A block diagram and the operation algorithm of the system are presented. The relationship between the results of low-frequency, high-frequency, and statistical processing of recorded data and the physical features of input signals is shown using as an example simulated acoustic signals in the various units of the designed system. The system was tested on real test objects in the case of three-point bending of nitrided steel specimens with various thicknesses of the hardened surface layer. It is shown that crack formation leads to emission of signals with an amplitudes of up to 4 V, whereas during deformation in the absence of pronounced cracking, the amplitude of the amplified signal does not exceed 0.5 V. DOI: 10.3103/S8756699011010146 Keywords: nondestructive testing, acoustic emission, digital processing, simulated signal, resonant sensor.
INTRODUCTION The acoustic emission (AE) method is a reliable method of nondestructive testing which has been extensively studied and presented in the scientific literature. It is based on the physical phenomenon related to the generation of mechanical (elastic) waves due to dynamic local changes in the internal structure of solids under loading [1]. The main advantage of AE diagnostics is that there is no need to have an access to all units under test, which reduces the effort of testing and simplifies the testing procedure. Designing equipment for the study of AE processes under laboratory conditions was considered in [2, 3] and some other papers. Modern AE systems designed using real-time digital signal processing methods allows one to discriminate AE signals from noise, determine the coordinates of the source, store the results of recording and measurements for subsequent detailed analysis, etc. Typically, such equipment is developed and adapted specifically for particular classes of industrial plants and tasks. Currently, devices of this kind are widely presented in the market of flaw-detection instruments of both domestic design and their foreign counterparts [4]. Devices of this level allow the user to identify defects (cracks) but they are not sensitive enough or not suitable for diagnosing the mechanical condition at earlier stages of deformation and prefracture. In experimental studies of the deformation behavior of structural materials, the AE method is very informative because it provides data from the entire working part of a loaded specimen during generation of deformation defects of the microscale level (primarily, dislocations and their ensembles, twins, etc.) [5, 6]. One of the most pressing research problems related to this method is the identification of acoustic emission 93
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sources [7–10], which is solved by extraction of individual AE signals (events) and their detailed analysis by signal processing equipment using data on signal amplitude, energy, frequency, damping, etc. From the point of view of development and implementation of research instruments for acoustic emission diagnostics, the problem is to record each single signal, classify it, and use to construct a unified picture of variation in acoustic emission parameters during deformation of the specimens studied. An example of research equipment available in the market and allowing a solution of such problems is the Vallen (Germany) device [11], whose operation algorithms are not discussed in the literature. Thus, an urgent problem is to develop cost-efficient research equipment for studying acoustic emission under laboratory conditions that can be adjusted to solve specific, more particular problems (according to the loading method and conditions, type of material, etc.). The basic steps in the development of such equipment include: designing hardware that have sufficient sensitivity, fast response, low noise, and great degree of discretization, developing and performing calibration procedures of the equipment; testing on simulated signals; experimental tests of the systems being developed; analysis of the traditionally used informative parameters described in the literature, search for recordable acoustic emission data that are new for analysis, interpretation of the obtained results in comparison with known data and within the scientific concepts relating the generation of acoustic emission with deformation characteristics at various scale levels, in particular, load diagrams at the macro level [12] and shear strain intensity at the mesoscopic scale [13, 14]. We note that the analysis and interpretation of data on acoustic emission are performed using various parameters [2, 3]. Thus, in [15], it is recommended to use: the number of pulses, total count, activity, the count rate and energy of acoustic emission, which was used in this work to develop a laboratory system for the analysis of AE signals. 1. ACOUSTIC EMISSION RECORDING SYSTEM The basic requirements for acoustic emission recording systems stem from features of AE signals, such as their complex nature, low energy level, broad frequency range, large dynamic range, etc. Therefore, the receiving transducer and electronic units must cause minimal distortions of the signal shape, show high sensitivity and high gain, and have minimal intrinsic noise. Because of the low energy of AE pulses, the sensitivity is as a rule improved using resonant transducers which have a very high output impedance of a few to several tens of megaohms, a wide range of operating frequencies (up to tens of megahertz), and a low noise level [16]. The frequency characteristic of acoustic emission is in the range of a few tens of kilohertz to several megahertz. These features of acoustic emission sensors (AESs) have determined the following requirements for the parameters of the measuring channel: high input impedance of the input amplifier needed to match the sensor to the amplifier input level, low noise level of the measuring channel reduced to the signal level at the channel input, and a wide frequency range with the maximum linear frequency response [17]. The basic units of the developed laboratory system for recording of acoustic emission are [14]: an AES, a broadband low-noise amplifier, a low-noise power supply, an analog-to digital converter (ADC), a PC, and software. Because of the absence of special requirements for the PC, we used a computer equipped with a Pentium 4 Core 2 Duo processor (2-GHz clock speed and 1 GB RAM) running Windows XP. Special restrictions were imposed on the choice of a broadband low-noise amplifier. Obviously, reliable recording of AE signals can only be achieved with minimal (a few microvolts) noise of the measuring channel reduced to the level of the amplifier input signal. The ready solutions available on the market did not meet these conditions. In this work, the amplifier was designed using the most advanced electronic units available in the market and the optimum circuit design. The power supply with low noise values brings the amplifier output noise close to the internal noise value. A feature of the circuit is the use of an AD8004 (Analog Devices) ultra low-noise broadband operational amplifier and a field-effect transistor at the amplifier input. The output impedance of the AES is high, and for the best matching to the amplifier input, it is necessary to ensure a high input impedance for the first amplifier of the cascade. The amplifier circuit was modeled with the P-SPICE software, traced using the P-CAD software, and was then produced in industrial environments. The working model provided a gain of about 54 dB at an intrinsic noise level below 12 µV, which is fully consistent with the design parameters, and the amplifier satisfies the specified conditions of experimental studies. In selecting hardware (HW) for the developed system (Fig. 1a) to improve the performance and reduce the cost of development and manufacture of individual units, we used the following devices: a GT-200 AES OPTOELECTRONICS, INSTRUMENTATION AND DATA PROCESSING
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(b) Start
HW AES
BLNA
ADC
Start of signal digitization
Low-frequency analysis
Waiting for an AO event
High-frequency analysis
Processing and storing
Postprocessing
Computer
SW DRU
prePU
LFAU
Choice of parameters for analysis
Initialization
UT
LNPS
HFAU
End of AE event
No
Display of parameter values
Äà SAU
postPU
DSU
95
DVU
No
End of digitization
Final Yes
Fig. 1. Block diagram of the laboratory system for recording and analysis of acoustic emission data: the interaction of the hardware units of the system (a); flow chart of the software algorithm for input, filtering, analysis, postprocessing, and visualization of AE signals (b).
[mounted on a unit under test (UT)] with an electroacoustic conversion coefficient of not less than 60 dB (GlobalTest, Sarov); a low-noise power supply (LNPS)—an alkaline battery with an amplitude of intrinsic noise of about 1 mV (Varta, Germany), a 14-bit LA-n150-14PCI ADC card with a conversion time of 143 ns at a maximum sampling frequency of 7 MHz (Rudnev–Shilyaev JSC, Moscow), and a broadband low-noise amplifier (BLNA) designed and made by the authors. The results of test measurements using the system show that the achieved performance allows high-sensitivity recording of AE signals with an amplitude of not less than 30 µV. The software (SW) for recording and processing signals and data in the developed facility includes the following procedures: 1) signal input [continuous information flow from the ADC card to the computer memory and recording data files are implemented by the data recording unit (DRU)]; 2) preprocessing [the preprocessing unit (prePU) analyzes digitized data flow to extract characteristic sequences of acoustic emission events in order to reduce the amount of information subject to storage and detailed analysis, the extracted AE signals are subject to further analysis]; 3) analysis/processing [by the low-frequency analysis unit (LFAU), the high-frequency analysis unit (HFAU), and the statistical analysis unit (SAU) with the extraction of informative parameters for the full evaluation of the recorded acoustic emission events, including the frequency, amplitude, duration, energy, damping coefficient, etc. for each individual event]; 4) postprocessing [this procedure involving image recognition elements and designed to eliminate (filter) AE signals not assigned to any of the possible classes of sources of acoustic emission is implemented by the postprocessing unit (postPU)]; 5) data storage [the data storage unit (DSU) records individual extracted AE signals as a digital sound file for subsequent retrospective analysis]; 6) graphic display [the data visualization unit (DVU) provides visualization of input information using available and our own software solutions and allows display of the results of a detailed analysis of data obtained using the system]. The software algorithm is shown in Fig. 1b. At the stage of initialization, the operation parameters of the ADC card are adjusted and the signal recording threshold is set. After the start of digitization , the system is rendered to the state of waiting for the signal start, which is defined as the predetermined recording threshold, and the signal end corresponds to its decrease below the predetermined threshold. For further more accurate analysis, a small fragment is recorded before the extracted signal and after it. Digitization is followed by the next step—analysis, for which informative features (parameters) are selected for the corresponding calculations. As a result of processing, the user gains access to numerical values of the selected parameters and a graphical visualization of the results. OPTOELECTRONICS, INSTRUMENTATION AND DATA PROCESSING
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AES C
Generator
Amplitude, V 0.6 0.4 0.2 0 _0.2 _0.4 _0.6
0
AES
Amplitude, V
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Time, ms Energy, dB
104
Amplitude, V
(d)
600
60 20 0 _20 _40
104
105 Frequency, Hz
Amplitude, V
(e)
0.8
1.0
0.4
0.5
0
0
_0.4
_0.5
1200 1800 2400
Energy, dB
106
AES
l
C
_0.8
40
105 Frequency, Hz
Generator
AES
800
1600 2400 3200
0
Time, ms
(h)
Energy, dB _20 _40 _60 _80 _100 _120 _140
106
(f)
_1.0 0
Time, ms
(g)
60 40 20 0 _20 _40
Signal modulation unit
Laboratory AE system
Signal modulation unit
(b) Laboratory AE system
(a)
104
800 1600 2400 3200
Time, ms Energy, dB
(i)
(j)
_40 _60 _80 _100 _120 _140 _160
105 Frequency, Hz
106
104
105 Frequency, Hz
106
Fig. 2. Block diagram of the experiment on generation and recording of simulated acoustic pulses: the emitter and receiver are located opposite to each other (a); are on the one side of the plate (b); a simulated rectangular pulse with a carrier frequency f = 100 kHz (c) and its spectrum (g); a simulated pulse with f = 100 kHz recorded for layout 1 (d) and its spectrum (h); a simulated pulse with f = 170 kHz received with layout 2, and its spectrum for l = 20 mm (e and i) and l = 180 mm (f and j).
2. TESTING OF THE LABORATORY SYSTEM USING SIMULATED SIGNALS The performance of the laboratory system was tested using various signal sources whose parameters were more or less close to those of real sources of acoustic emission. The results of this testing are given below. 2.1. Formation of a Simulated Signal with a Resonant Sensor The experiment was performed by analogy with the studies described in [18]. The difference was that in the present work, we used a high-frequency generator which provided reference-input signal of amplitude and frequency. The tests were performed at different positions of the emitter and receiver: in the first case (layout 1), they were located opposite to each other (Fig. 2a). The material for the transmission of the signal was a plate of D16AT aluminum alloy 2 mm thick. In the second case (layout 2), they were located on the same side of this plate at a distance l = 10–100 mm apart (Fig. 2b). Two GT-200 AES were used as the signal emitter and receiver. The emitter was mounted on a metallic cone (C) of hardened steel to localize acoustic radiation at the point of its contact with the plate to simulate a point source of acoustic emission. The source of the carrier signal was a GZ-111 generator of sinusoidal signals. A signal modulation unit (see Figs. 2a and 2b) based on an asymmetric logic gate multivibrator was used for the amplitude modulation of the carrier signal. In the first series of experiments with layout 1, we investigated the propagation of a signal with a frequency f = 100 kHz (see Fig. 2a). The signal is shown in Fig. 2c and has the following characteristics: OPTOELECTRONICS, INSTRUMENTATION AND DATA PROCESSING
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(b)
_38
Frequency, kHz
_40 Energy, dB
_165.5 _154.9 _144.3 _133.7 _123.0 _112.4 _101.8 _91.19 _80.58 _69.96 _59.35 _48.73 _38.12 _27.50
400
_42 _44 _46 _48
300
200
100 _50 0
2
4
6
8 10 12 Distance, cm
14
16
18
50
100 150 200 250 300 400 Frequency, kHz
Fig. 3. Variation in the energy of the main peak in the spectrum of the input signal: as a function of the distance l from the receiver to the source (a) and as a function of the frequency of a given signal (b). Table 1. Parameters of experiments with simulated signals generated by a resonant sensor No.
Range, kHz
Frequency range, kHz
1
50–160
10
2
165–190
5
3
200–300
10
4
320–400
20
signal frequency 100 kHz, pulse repetition period 1 s, signal amplitude at the peak 0.5 V. The initial signal simulates a sharp increase in the amplitude of the ultrasonic signal propagating in a solid. Signal processing was performed with the HFAU software (see Fig. 1a). Figures 2d and 2h give the signal recorded by the AES, and its frequency spectrum. The presence of an additional spectral peak at a frequency of ∼ 180 kHz is due to the resonant AES, whose frequency corresponds to this value. These results indicate that during propagation of the simulated signal through the medium and its recording with layout 1, the basic (carrier) frequency is preserved and its energy in the spectrum has the maximum value, and at the same time, an additional peak appears whose frequency corresponds to the resonance frequency for the AES (see Figs. 2d and 2h and Fig. 3a). The amplitude of the central maximum decreases slightly with respect to its initial value (compare Figs. 2g and 2h). In addition, there is a significant increase in the role of transition processes related to the passage of elastic waves from the emitter to the medium and from the plate to the receiver. This is manifested in a marked distortion of the leading and trailing pulse edges (see Fig. 2d). The second series of experiments was performed with layout 2 of the arrangement of the emitter and receiver on the same side of the plate (see Fig. 2b). The path of the simulated signal is extended, resulting in a more marked distortion of the signal shape, which differs significantly from the shape of the test signal (see Fig. 2e and c). Furthermore, with increasing distance between the AESs, this distortion only increased (Fig. 2f). For this reason, the carrier frequency of the input signal was chosen close to the resonance frequency for the AES: f = 170 kHz. The spectra of the signals shown in Figs. 2i and 2j indicate that they have only one peak that corresponds to the resonance frequency of the GT-200 sensor. The energy of the main peak in the spectrum is analyzed as a function of the distance l between the emitter and receiver (see Fig. 3a). It is seen that the energy of the maximum peak in the spectrum that corresponds to the resonant frequency of the AES fp AE ∼ 180 kHz remains almost unchanged with increasing distance between the sensors. In the third series of experiments, performed with layout 1, an amplitude-modulated signal in the form of rectangular pulses with a carrier frequency of 50–400 kHz was applied to the emitter (Table 1). Figure 3b shows the variation in the energy of the main frequency maximum of the received AE frequency versus input signal frequency. The following features are clearly seen: 1) the range of frequencies contains the maximum f ∼ 180 kHz, which is the resonant frequency of the AES (fp AE ∼ 180 kHz); OPTOELECTRONICS, INSTRUMENTATION AND DATA PROCESSING
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2) the peak energy at the carrier frequency is different at different frequencies, which is most likely due to the nonlinear response of the GT-200 AES [16]; 3) in the frequency range f ≤ 100 kHz, the carrier frequency peak in the spectrum is weakly pronounced. We note that the presence of the resonant frequency of the AESs in the spectrum is due to their use as emitter and receiver. In the case of minimal distortions of the simulated signal during the direct passage through the plate (layout 1), the carrier frequency is maintained at a level acceptable for discrimination. When the signal travels a distance l in the aluminum plate (layout 2), the signal shape is markedly distorted, resulting in the main peak in the frequency spectrum corresponding to fp AE ∼ 180 kHz. At the same time, the energy of this peak remains almost constant with increasing distance between the emitter and receiver. Thus, the signals used in the work only simulate the real AE signals and may well differ from it in the nature of propagation in the medium. Nevertheless, for the purpose of testing the system, this model seems quite correct. Moreover, the proposed procedure allows one to calibrate the laboratory system and, using the known response of the sensor, to determine the true frequencies characteristic of acoustic emission sources. 2.2. Simulating an AE Signal Source by Breaking a Graphite Lead This method is recommended for calibration of the AES [19, 20]. In this case, the acoustic signal arising from the breaking of the lead is also a simulated one. The objectives of this work were: to compare the signals emitted during breaking of leads of various hardness, and to determine the effect of lead hardness on the formation of the simulated signal (and its spectrum) recorded using the system. The signal was processed by the HFAU (see Fig. 1a). In the experiments, we used leads (diameter 1.9 mm) of KOH-I-NOOR (Czech Republic) pencils with hardness of 2H to 3B, according to the pencil hardness notation. A plate of D16AT aluminum alloy 2-mm thick was used as an acoustically conducting medium. The AES was fixed at a distance of 140 mm from the signal source. A layer of epoxy resin was applied between the sensor and the plate to provide the best acoustic contact. Breaking of the lead was implemented as is described in [21]. To establish the relationship between the specified hardness of the lead and its microhardness, we performed measurements using a PMT-3 microhardness tester. The results are shown in Fig. 4a. We note that the lead microhardness increases almost linearly. A typical signal recorded by the system during breaking of leads, and its spectrum are given in Figs. 4b and 4c. It is evident that individual expressed frequency peaks cannot be distinguished in the spectrum. This indicates that the spectrum of the signal emitted during breaking contains almost all frequencies in a varying degree. The maximum energy of the spectrum that can be distinguished most likely corresponds to the AES resonant frequency fp AE ∼ 180 kHz (shown by an arrow in Fig. 4c). An analysis of the results of measuring the energy spectrum in the case of breaking leads of various hardness shows that with increasing the lead hardness, the spectral energy increases and this can be satisfactorily approximated by a linear relation (Fig. 4d). This result is quite correct and expected: harder materials have greater capability for generating acoustic emission (see, e.g., [5, 6]). 2.3. Simulating an AE Signal Source by Dropping a Bearing Ball In this work, we used one more method to record simulated AE signals, whose prototype is described in [22] as an energetic method for calibrating AESs. A simulated signal in the form of an acoustic wave was produced by dropping a steel ball. According to [22], the signal energy can be calculated by the formula T EAE =
V (t)2 dt,
(1)
0
where V (t) is the time-dependent voltage from the AES and T is the duration of the AE pulse. The parameter calculated by formula (1) is also called the measured area of the rectified signal envelope (MARSE). Supposing that most of the potential energy of a falling ball is transformed to the energy of elastic waves and that the waves produced by the impact of the ball are converted by the sensor to an electrical signal similarly to the waves formed by real AE sources, we assume that the dependence of the mass of the ball is proportional to the MARSE parameter for acoustic emission [22]. In the experiments, we used rolling bearing balls of ShKh15 steel. The masses of the balls are presented in Table 2. Ball 3 was dropped onto a metal base 1 (D16AT duralumin plate 1 mm thick) from a height of 100 mm in the region located at a distance of ∼ 100 mm from the fixed AES 2 (Fig. 5a). Since the amplitude of the signal recorded by the sensor was initially high enough, it was connected to the data acquisition board OPTOELECTRONICS, INSTRUMENTATION AND DATA PROCESSING
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2B
B HB H Pencil hardness
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0
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40 30 20 10 0 _10 _20
Signal energy, rel. units
Energy, dB
60 50
4
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5
6
10 Frequency, Hz
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10
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B
HB H 2H Pencil hardness
Fig. 4. Experimental results on simulation of acoustic emission signals by breaking a graphite lead: microhardness versus pencil lead hardnesss (a); typical signal recorded by the GT-200 AES during breaking of a graphite lead of HB hardness and 1.9 mm in diameter (b); frequency spectrum (c); energy spectrum of the AE signal versus pencil lead hardness (d). Table 2. Characteristics of steel balls and the energy of the elastic-wave spectrum produced by their impact
No.
Ball diameter, mm
Ball mass, g
Relative energy
1
3.0
0.110
6.64 · 106
2
4.0
0.263
1.27 · 107
3
5.0
0.440
1.80 · 107
4
5.5
0.698
2.84 · 107
5
8.0
2.029
8.02 · 107
6
9.5
3.535
1.24 · 108
without using the BLNA block (see Fig. 1a). Each ball was dropped at least 30 times. A typical signal recorded by the developed acoustic emission system and its Fourier spectrum are given in Figs. 5b and 5c. Calculated energies are listed in Fig. 5d. An analysis of the data shows that the energy of the signal (elastic wave) produced by the impact of a steel ball on a plate varies linearly with increasing mass of the ball (see Fig. 5d). Thus, in this case, in addition to employing the already known method of calibrating AESs, we obtained a linear relationship between the spectrum energy and the ball mass, which will allow the developed procedure to be used as a rapid method for calibrating the system before measurements. OPTOELECTRONICS, INSTRUMENTATION AND DATA PROCESSING
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3 Amplitude, V
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Energy, dB
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105 Frequency, Hz
106
(d)
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2 Mass, g
3
4
Fig. 5. Experiment with free-falling balls: Diagram of the experiment (a); the characteristic signal arising from the impact of a ball of mass 3 g (b) and its spectrum (c); mass of a falling ball versus total energy of the AE signal spectrum (d).
2.4. Recording AE Signals during Deformation of Specimens with a Brittle Coating As a version of the manufacture of test specimens for nondestructive testing, it is recommended that steel specimens be nitrided under known thermochemical treatment conditions [23]. Loading, for example, due to impact of ball, results in the formation of a system of cracks of known depth in such specimens, and for the method of penetrating substances, this pattern is recommended as a reference. In this work, hardened layers of various thicknesses on the surface of steel specimens were produced by nitriding in a glow discharge plasma. This allows one, using metallographic analysis, to estimate the number of cracks formed in a deformed specimen and compare them with the number of AE signals with an elevated amplitude. Thus, in addition to the proper purpose of calibrating the system for recording acoustic emission, it is possible to obtain information important for the subsequent identification of signals induced by deformation or fracture [24]. For the experiment, we manufactured specimens of steel 20Kh13, which were nitrided with an exposure of 30 and 120 min; the calculated thickness of the nitrided layer was 5 and 50 µm, respectively. The specimen size was 60 × 18 × 1.5 mm. The specimens were tested for three-point bending using an Instron 5582 facility. The distance between fixed beams was 30 mm [25]. Electron micrographs of the nitrided surface for deformation of both specimens with a brittle coating are presented in Figs. 6a and 6b. It is evident that in the thick hardened surface layer, loading resulted in the formation of cracks whose depth is approximately equal to the layer thickness (see Fig. 6b). Data on the counting rate of AE signals (obtained using the LFAU, Fig. 1a) for specimens of both types are shown in Fig. 6c and 6d. For specimens with a nitrided layer of small thickness (see Fig. 6a), the acoustic emission intensity did not exceed 2 s−1 (see Fig. 6c). The total number of signals recorded during the experiment was about 350. For specimens with a nitrided layer of 50 µm (see Fig. 6b), the total number of recorded AE signals was about 6000 (see Fig. 6d). OPTOELECTRONICS, INSTRUMENTATION AND DATA PROCESSING
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Number of AE signals, pieces
Number of AE signals, pieces
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6
40
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äNAE/ät
8
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101
600 500 400 300 200 100 0
0
200
400
600
800 t
(f) 40 20 0 300
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350
400
500 100 200 300 400 Modulus of the amplitude of AE signals, mV
Fig. 6. Scanning electron micrographs of the nitrided surfaces of the specimens after the tests: a layer of 5 µm (a) and a layer of 50 µm (b); counting rate of AE pulses versus loading time of (c and d); bar chart of AE signal amplitude for specimens with thin and thick layers (e and f).
The signal amplitude distribution was analyzed using the SAU (see Fig. 1a). The results showed that for specimens with a thick nitrided layer, there were a considerable number of AE pulses with an amplitude of 3–4 V, which, according to most of the literature data and studies of [24], should be associated with the formation of cracks. At the same time, for the specimen with a thin nitrided layer, in which the formation of cracks was not found metallographically (see Fig. 6b), a much smaller number of AE pulses were recorded, among which no pulse with an amplitude larger than 500 mV was detected (see Fig. 6). The approach used in this experiment is not fundamentally new. Acoustic emission sources are classified according to the amplitude and frequency of dislocation type sources: twinning, microcracking, macrocrack etc. [24]. However, a detailed physical examination was beyond the scope of this work. In addition, it is shown that the number of microcracks is not identical to the number of AE signals with the maximum amplitude (energy). This is due to the fact that the development of a continuous transverse cracks leads to the generation of several dozens of high-power AE pulses since crack opening proceeds throughout the width of the specimen. OPTOELECTRONICS, INSTRUMENTATION AND DATA PROCESSING
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PANIN et al. CONCLUSIONS
In this study, two practical tasks were solved. A laboratory system for recording and analysis of acoustic emission data for input signals with a minimum recorded amplitude of 30 µV was designed and produced using electronic units and sensors available on the market and the amplifying unit designed by the authors. The system was tested using software developed by the authors and well-known methods for generating simulated AE signals. Traditional signal preprocessing and postprocessing units and the recording of event accumulation and separate spectral analysis of each signal used in the AE method were implemented within the framework of software development. A flow chart of the algorithm for selecting single events of acoustic emission was proposed. It was shown that the propagation of simulated signals through a D16AT plate caused a significant distortion of the leading and trailing edges of a rectangular modulated pulse with the preservation of its basic (carrier) frequency. In this case, the energy of the transmitted and received signals differed by 5%. During propagation in the plate, the signal shape was significantly distorted, which was manifested in the spectrum as a single pronounced peak corresponding to the resonant frequency of the AES. Changing the distance between the emitter and the receiver did not affect the energy of this resonant frequency peak. The experiments showed that increasing the hardness of the pencil lead, first, increased the energy of the acoustic signal emitted during its breaking, and second, it was proportional to the increase in the energy of the signal recorded by the AES. By measuring the energy of the elastic wave produced by the impact of a bearing ball on the plate and recorded with the developed system, it was shown that the energy of the signal spectrum is linearly related to the mass of the ball. The AE signals caused by crack formation in the nitrided layer and by plastic deformation of steel specimens were discriminated from each other using the system and software units. A significant difference in the count rate of acoustic emission events was observed during three-point bending of specimens with various thicknesses of the brittle hardened surface layer subjected to cracking. The authors are grateful to B. B. Ovechkin (National Research Tomsk Polytechnical University) for providing nitrided specimens, whose images were obtained using a LEO EVO 50 (Germany) scanning electron microscope at the Center of Collective Use of the Institute of Strength Physics and Materials Science of the Siberian Branch of the Russian Academy of Sciences. This work was supported by Siberian Branch of the Russian Academy of Sciences (project No. III.20.1.3.) and the Council for Grants of the President of the Russian Federation (No. NSh-5242.2010.1). REFERENCES 1. V. I. Ivanov and V. M. Belov, Acoustic-Emission Control of Welding and Welded Joints (Mashinostroenie, Moscow, 1981) [in Russian]. 2. “JSC Special Diagnostic Systems,” http://www.sds.ru (date accessed: 01.12.2010). 3. H. A. Semashko, O. B. Bashkov, B. N. Marin, et al., “Deformation Monitoring and Prediction of the Limiting Characteristics of Materials Using the Acoustic Emission Method,” in Proc. Interregional Conf., KnAAPO, Khabarovsk, 2001, pp. 110–116. 4. E. S. Nikitin, B. V. Shubin, and A. G. Lunev, “Device for Acoustic-Emission Diagnostics,” in Scientific Session TUSUR-2007 (Izd. V-Spectr, Tomsk, 2007), Part 4, pp. 20–23 [in Russian]. 5. N. A. Bunin, Investigation of Plastic Deformation of Metals by the Acoustic Emission Method (Izd. LGU, Leningrad, 1990) [in Russian]. 6. V. A. Greshnikov and Yu. B. Drobot, Acoustic Emission (Izd. Standartov, Moscow, 1976) [in Russian]. 7. O. Bashkov, S. Panin, N. Semashko, and D. Shpak, “A Method for Locating Acoustic Emission Signal Sources by a Single Sensor,” in Proc. of the 19th Int. Acoustic Emission Symp. (IAES-19), Kyoto University, Japan, 9–12 December, 2008, pp. 12–18. 8. G. B. Muravin, Ya. V. Simkin, and A. I. Merman. “Identification of Material Fracture Mechanisms by Spectral Analysis of Acoustic Emission Signals,” Defektoskopiya, No. 4, 8–15 (1989). 9. N. V. Novikov, S. F. Filonenko, N. I. Gorodovskii, and V. S. Biryukov, “On the Criterion of Determining the Source of AE Signals during Loading of Materials,” Sverkhtverd. Mater., No. 2, 42–45 (1987). 10. Y. Nakamura, C. L. Veach, and B. O. McCauley, “Amplitude Distribution of Acoustic Emission Signals,” in Acoustic Emission—STP 505, Baltimore, USA, 1972, pp. 164–186. OPTOELECTRONICS, INSTRUMENTATION AND DATA PROCESSING
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