• The Journal of Engineering Geology
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• v.25 no.4
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• pp.587-598
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• 2015

We examined various existing threshold methods for the determination of the first arrival time of acoustic emission (AE), and developed a new variable threshold method that could determine the first arrival time of AE more accurately and more quickly than existing methods. The new method, a modification of an existing threshold method, does not fix the threshold, but applies variable thresholds for the AE signals according to noise analysis. Two- and three-dimensional models were established to test the effectiveness of the new method. It could determine source locations of AE in a two-dimensional model 38.3% more accurately than the pre-existing threshold methods. Its accuracy improvement over the existing methods in a three-dimensional model was about 15.2%. A practical test involved measuring the source locations of AE during three-point bending tests of granite cores. The new method placed the sources closer to the fracture plane than did the pre-existing methods, indicating its superior (and quicker) ability to determine the source locations of AE.

• Tunnel and Underground Space
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• v.34 no.2
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• pp.169-184
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• 2024

This study investigates acoustic emission (AE) and micro-deformation characteristics of circular openings through biaxial compression experiments. The experimental results showed a significant increase in the frequency, count, energy, and amplitude of AE signals immediately before damage occurred in the circular opening. The differences in frequency and count between before and after damage initiation were significantly pronounced, indicating suitable factors for identifying damage occurrence in circular openings. The results for digital image correlation (DIC) technique revealed that micro-deformation was concentrated around the openings, as evidenced by the spatial distribution of strain. In addition, spalling was observed at the end of the experiments. The AE and micro-deformation characteristics presented in this study are expected to serve as fundamental data for evaluating the stability of underground openings and boreholes for deep subsurface projects.

• Structural Engineering and Mechanics
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• v.63 no.3
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• pp.361-370
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• 2017

Through the use of finite element analysis and acoustic emission techniques we have evaluated the interfacial failure of a carbon fiber reinforced polymer (CFRP) repair patch on a notched aluminum substrate. The repair of cracks is a very common and widely used practice in the aeronautics field to extend the life of cracked sheet metal panels. The process consists of adhesively bonding a patch that encompasses the notched site to provide additional strength, thereby increasing life and avoiding costly replacements. The mechanical strength of the bonded joint relies mainly on the bonding of the adhesive to the plate and patch stiffness. Stress concentrations at crack tips promote disbonding of the composite patch from the substrate, consequently reducing the bonded area, which makes this a critical aspect of repair effectiveness. In this paper we examine patch disbonding by calculating the influence of notch tip stress on disbond area and verify computational results with acoustic emission (AE) measurements obtained from specimens subjected to uniaxial tension. The FE results showed that disbonding first occurs between the patch and the substrate close to free edge of the patch followed by failure around the tip of the notch, both highest stress regions. Experimental results revealed that cement adhesion at the aluminum interface was the limiting factor in patch performance. The patch did not appear to strengthen the aluminum substrate when measured by stress-strain due to early stage disbonding. Analysis of the AE signals provided insight to the disbond locations and progression at the metal-adhesive interface. Crack growth from the notch in the aluminum was not observed until the stress reached a critical level, an instant before final fracture, which was unaffected by the patch due to early stage disbonding. The FE model was further utilized to study the effects of patch fiber orientation and increased adhesive strength. The model revealed that the effectiveness of patch repairs is strongly dependent upon the combined interactions of adhesive bond strength and fiber orientation.

• Composites Research
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• v.14 no.3
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• pp.18-31
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• 2001

Interfacial and microfailure properties of the modified steel, carbon and glass fibers/cement composites were investigated using electro-pullout test under tensile and compressive tests with acoustic emission (AE). The hand-sanded steel composite exhibited higher interfacial shear strength (IFSS) than the untreated and even neoalkoxy zirconate (Zr) treated steel fiber composites. This might be due to the enhanced mechanical interlocking, compared to possible hydrogen or covalent bonds. During curing process, the contact resistivity decreased rapidly at the initial stage and then showed a level-off. Comparing to the untreated case, the contact resistivity of either Zr-treated or hand-sanded steel fiber composites increased to the infinity at latter stage. The number of AE signals of hand-sanded steel fiber composite was much more than those of the untreated and Zr-treated cases due to many interlayer failure signals. AE waveforms for pullout and frictional signals of the hand-sanded composite are larger than those of the untreated case. For dual matrix composite (DMC), AE energy and waveform under compressive loading were much higher and larger than those under tensile loading, due to brittle but well-enduring ceramic nature against compressive stress. Vertical multicrack exhibits fur glass fiber composite under tensile test, whereas buckling failure appeared under compressive loading. Electro-micromechanical technique with AE can be used as an efficient nondestructive (NDT) method to evaluate the interfacial and microfailure mechanisms for conductive fibers/brittle and nontransparent cement composites.

• Journal of the Korea Concrete Institute
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• v.21 no.5
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• pp.589-597
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• 2009

High Performance Fiber-reinforced Cement Composite (HPFRCC) shows the multiple crack and damage tolerance capacity due to the interfacial bonding of the fibers to the cement matrix. For practical application, it is needed to investigate the fractural behavior of HPFRCC and understand the micro-mechanism of cement matrix with reinforcing fiber. This study is devoted to the investigation of the AE signals in HPFRCC under monotonic and cyclic uniaxial compressive loading, and total four series were tested. The major experimental parameters include the type and volume fraction of fiber (PE, PVA, SC), the hybrid type and loading pattern. The test results showed that the damage progress by compressive behavior of the HPFRCC is a characteristic for the hybrid fiber type and volume fraction. It is found from acoustic emission (AE) parameter value, that the second and third compressive load cycles resulted in successive decrease of the amplitude as compared with the first compressive load cycle. Also, the AE Kaiser effect existed in HPFRCC specimens up to 80% of its ultimate strength. These observations suggested that the AE Kaiser effect has good potential to be used as a new tool to monitor the loading history of HPFRCC.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.184-192
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• 2022

Valve internal leakage is caused by damage to the internal parts of the valve, resulting in accidents and shutdowns of the piping system. This study investigated the possibility of a real-time leak detection method using the acoustic emission (AE) signal generated from the piping system during the internal leakage of a butterfly valve. Datasets of raw time-domain AE signals were collected and postprocessed for each operation mode of the valve in a systematic manner to develop a data-driven model for the detection and classification of internal leakage, by applying machine learning algorithms. The aim of this study was to determine whether it is possible to treat leak detection as a classification problem by applying two classification algorithms: support vector machine (SVM) and convolutional neural network (CNN). The results showed different performances for the algorithms and datasets used. The SVM-based binary classification models, based on feature extraction of data, achieved an overall accuracy of 83% to 90%, while in the case of a multiple classification model, the accuracy was reduced to 66%. By contrast, the CNN-based classification model achieved an accuracy of 99.85%, which is superior to those of any other models based on the SVM algorithm. The results revealed that the SVM classification model requires effective feature extraction of the AE signals to improve the accuracy of multi-class classification. Moreover, the CNN-based classification can be a promising approach to detect both leakage and valve opening as long as the performance of the processor does not degrade.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.5
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• pp.765-771
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• 2001

Elastic-plastic fracture toughness JIC can be used a s an effective design criterion in elastic-plastic fracture mechanics. Among the JIC test methods approved by ASTM, unloading compliance method was used in this study. In order to examine the relationship between fracture behavior of JIC test and AE signals, the post processing of AE signals has been carried out by Short Time Fourier Transform(STFT), one of the time-frequency analysis methods. The objective of this study is to evaluate the application of characterization of AE signals for unloading compliance method of JIC test. As a result of time-frequency analysis, we could extract the AE from the raw signal and analyze the frequencies in AE signal at the same time. AE signal generated by elastic-plastic fracture of material has some different aspects at elastic and plastic ranges, or the first portion of crack growth by fracture. First of all, increased energy recorded and detected by using AE count method increase rapidly from the start of ductile fracture. The variation of main frequency range with time-frequency analysis method could be confirmed. We could know fracture behavior of interior material by examination AE characteristics generated in real-time when elastic-plastic fracture occurred in material under loading.

• The Journal of Engineering Geology
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• v.25 no.1
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• pp.103-113
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• 2015

This study presents a novel algorithm for determining the P-wave arrival time using amoving window function to improve source localization in low-SNR (signal-to-noise ratio)acoustic emissions. The proposed algorithm was applied to low-SNR signals to verify the accuracy of measurements against existing algorithms. When other algorithms were applied, the test results revealed that SNR decreased and accuracy was reduced, especially where SNR wasless than 2.14. The proposed algorithm using amoving window function considers the frequency characteristic and signal amplitude simultaneously, and produced reliable results where SNR was 2.14.

• Journal of the Korean Wood Science and Technology
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• v.31 no.2
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• pp.84-91
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• 2003

In this study, the three species (Populus euramericana, Pinus densiflora and Quercus variabilis) were cut to difference (0, 0.15, 0.3, 0.45 mm) between the size of tip and that of root of the finger (DSTR) and jointed with poly vinyl acetate (PVA) and resorcinol-phenol resin (RPR). We described the relationship between the bending strength properties of finger DSTR and the acoustic emission (AE) generated during the bending test. The results were as follows: The AE generation time of finger-jointed specimens with RPR adhesive was earlier than that with PVA adhesive. The AE cumulative event count of finger-jointed specimens with RPR adhesive continuously increased with increasing load and the event count was much more than that with PVA adhesive. Also, the AE cumulative event count for resorcinol-phenol resin adhesive obtained from low load level was abundant. The AE wave in finger-jointed specimens with RPR adhesive could be detected in the below proportional limit load. Therefore, AE signals from bending test are useful for the estimation of strength in finger DSTR specimens.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.4
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• pp.601-607
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• 2012

The magnetic abrasive polishing (MAP), for online monitoring with AE sensor attachment, was performed in this study. To predict the surface roughness after the magnetic abrasive polishing of NAK80, the signal data acquired from the AE sensor were analyzed. A dimensionless coefficient, which consisted of average of AErms and standard deviation of AE signal, was defined as a characteristic of the MAP and a prediction model was obtained using least square method. A neural network, which had multiple input parameters from AE signals and polishing conditions, was applied for predicting the surface roughness. As a result of this study, it was seen that there was very close correlation between the AE signal and the surface roughness in the MAP. And then on-line prediction of the surface roughness after the MAP of the NAK80 was possible by the developed prediction model.