• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.40-48
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• 2004

The object of this study is to investigate the effect of joinability by using acoustic emission (AE) signals and doing a pattern recognition for weld heat affected zone (HAZ) in tensile testing. This study was carried out an SWS 490A high tension steel for electric shielded metal arc welding (SMAW), $CO_2$ gas arc welding and TIG welding. And correspondingly, the root openings are 3, 4 and 2.8mm. The results of the tensile test of weld HAZ come out electric shield arc welding > $CO_2$ gas arc welding > TIG welding in case of single welding. It is believed that this is a phenomenon where difference of its root opening or base metal thickness. Also, the technique of AE is ideally suited to study variables which control time and stress dependent fracture or damage process in metallic materials.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.2
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• pp.170-177
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• 2009

The objective of this study is to evaluate an acoustic emission (AE) source characterization and fracture behavior of the SM45C steel by using back-propagation neural network (BPN). In previous research Ref. [8] about k-nearest neighbor classifier (k-NNC) continuity, we used K-means clustering method as an unsupervised learning method for obtaining multi-variate AE main data sets, such as AE counts, energy, amplitude, risetime, duration and counts to peak. Similarly, we applied k-NNC and BPN as a supervised learning method for obtaining multi-variate AE working data sets. According to the error of convergence for determinant criterion Wilk's ${\lambda}$, heuristic criteria D&B(Rij) and Tou values are discussed. As a result, in k-NNC before fracture signal is detected or when fracture signal is detected, showed that produce some empty classes in BPN. And we confirmed that could save trouble in AE signal processing if suitable error of convergence or acceptable encoding error give to BPN.

• Journal of the Korea Safety Management & Science
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• v.11 no.1
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• pp.27-33
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• 2009

A study of fracture to material is getting interest in nuclear and aerospace industry as a viewpoint of safety. Acoustic emission (AE) is a non-destructive testing and new technology to evaluate safety on structures. In previous research continuously, all tensile tests on the pre-defected coupons were performed using the universal testing machine, which machine crosshead was move at a constant speed of 5mm/min. This study is to evaluate an AE source characterization of SM45C steel by using k-nearest neighbor classifier, k-NNC. For this, we used K-means clustering as an unsupervised learning method for obtained multi -variate AE main data sets, and we applied k-NNC as a supervised learning pattern recognition algorithm for obtained multi-variate AE working data sets. As a result, the criteria of Wilk's $\lambda$, D&B(Rij) & Tou are discussed.

• Journal of Ocean Engineering and Technology
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• v.17 no.3 s.52
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• pp.46-51
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• 2003

Acoustic emission (AE) signals, emanated during local failure of aluminum alloys, have been the subject of numerous investigations. It is well known that the characteristics of AE are strongly influenced by the previous thermal and mechanical treatment of the sample. Possible sources of AE during deformation have been suggested as the avalanche motion of dislocations, fracture of brittle particles, and debonding of these particles from the alloy matrix. The goal of the present study is to determine if AE occurring as the result of fatigue crack propagation could be evaluated by the joint time-frequency analysis method, short time Fourier transform (STFT), and Wigner-Ville distribution (WVD). The time-frequency analysis methods can be used to analyze non-stationary AE more effectively than conventional techniques. STFT is more effective than WVD in analyzing AE signals. Noise and frequency characteristics of crack openings and closures could be separated using STFT. The influence of various fatigue parameters on the frequency characteristics of AE signals was investigated.

• Geomechanics and Engineering
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• v.14 no.2
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• pp.203-209
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• 2018

Reliable estimation of compressive as well as tensile in-situ stresses is critical in the design and analysis of underground structures and openings in rocks. Kaiser effect technique, which uses acoustic emission from rock specimens under cyclic load, is well established for the estimation of in-situ compressive stresses. This paper investigates the Kaiser effect on marble specimens under cyclic uniaxial compressive as well as cyclic uniaxial tensile conditions. The tensile behavior was studied by means of Brazilian tests. Each specimen was tested by applying the load in four loading cycles having magnitudes of 40%, 60%, 80% and 100% of the peak stress. The experimental results confirm the presence of Kaiser effect in marble specimens under both compressive and tensile loading conditions. Kaiser effect was found to be more dominant in the first two loading cycles and started disappearing as the applied stress approached the peak stress, where felicity effect became dominant instead. This behavior was observed to be consistent under both compressive and tensile loading conditions and can be applied for the estimation of in-situ rock stresses as a function of peak rock stress. At a micromechanical level, Kaiser effect is evident when the pre-existing stress is smaller than the crack damage stress and ambiguous when pre-existing stress exceeds the crack damage stress. Upon reaching the crack damage stress, the cracks begin to propagate and coalesce in an unstable manner. Hence acoustic emission observations through Kaiser effect analysis can help to estimate the crack damage stresses reliably thereby improving the efficiency of design parameters.

• Tribology and Lubricants
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• v.35 no.5
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• pp.317-322
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• 2019

In spite of progress in tribological research, machine component failure due to friction and wear has been reported frequently. This failure may lead to secondary damage that can cause huge expense for maintenance and repair. To prevent economic loss, it is important to detect and predict the initial failure point. In this sense, various researchers have been tried to develop Condition Monitoring (CM) method using Acoustic Emission (AE) generated while the materials undergo failure. In this study, effect of particles on friction and wear was investigated using the pin-on-plate friction test and AE signal was recorded with a band-width type AE sensor. The experiments were performed in dry and lubricant conditions using steel and glass as specimens. After the experiment, 3D laser microscope image was captured to evaluate the wear behavior quantitatively. The AE signal was analyzed in time-domain and frequency-domain. The amplitude was compared with the frictional results. The results of this study showed that particle generation accelerate wear, generate high magnitude AE signal and change the frequency characteristics of the signal. Also, lubricant condition test results showed low coefficient of friction, low wear rate, and low magnitude of AE signal compared to the dry condition. It is expected that the results of this study will aid in better assessment of wear in CM technology

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.19 no.1
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• pp.75-85
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• 2021

Evaluating the quantitative damage to rocks through acoustic emission (AE) has become a research focus. Most studies mainly used one or two AE parameters to evaluate the degree of damage, but several AE parameters have been rarely used. In this study, several data-driven models were employed to reflect the combined features of AE parameters. Through uniaxial compression tests, we obtained mechanical and AE-signal data for five granite specimens. The maximum amplitude, hits, counts, rise time, absolute energy, and initiation frequency expressed as the cumulative value were selected as input parameters. The result showed that gradient boosting (GB) was the best model among the support vector regression methods. When GB was applied to the testing data, the root-mean-square error and R between the predicted and actual values were 0.96 and 0.077, respectively. A parameter analysis was performed to capture the parameter significance. The result showed that cumulative absolute energy was the main parameter for damage prediction. Thus, AE has practical applicability in predicting rock damage without conducting mechanical tests. Based on the results, this study will be useful for monitoring the near-field rock mass of nuclear waste repository.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.19 no.2
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• pp.197-204
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• 2021

In this study, the well-known non-destructive acoustic emission (AE) and electrical resistivity methods were employed to predict quantitative damage in the silo structure of the Wolsong Low and Intermediate Level Radioactive Waste Disposal Center (WLDC), Gyeongju, South Korea. Brazilian tensile test was conducted with a fully saturated specimen with a composition identical to that of the WLDC silo concrete. Bi-axial strain gauges, AE sensors, and electrodes were attached to the surface of the specimen to monitor changes. Both the AE hit and electrical resistance values helped in the anticipation of imminent specimen failure, which was further confirmed using a strain gauge. The quantitative damage (or damage variable) was defined according to the AE hits and electrical resistance and analyzed with stress ratio variations. Approximately 75% of the damage occurred when the stress ratio exceeded 0.5. Quantitative damage from AE hits and electrical resistance showed a good correlation (R = 0.988, RMSE = 0.044). This implies that AE and electrical resistivity can be complementarily used for damage assessment of the structure. In future, damage to dry and heated specimens will be examined using AE hits and electrical resistance, and the results will be compared with those from this study.

• Geomechanics and Engineering
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• v.37 no.2
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• pp.149-166
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• 2024

The acoustic emission (AE) technique is utilized to estimate the rock failure status in underground spaces. Understanding the AE characteristics under loading conditions is essential to ensure the reliability of AE monitoring. The AE characteristics depend on the material properties (p-wave velocity, density, UCS, and Young's modulus) and damage stages (stress ratio) of the target rock mass. In this study, two groups of granite specimens (based on the p-wave velocity regime) were prepared to explore the effect of material properties on AE characteristics. Uniaxial compressive loading tests with an AE measurement system were performed to investigate the effect of the rock properties using AE indices (count index, energy index, and amplitude index). The test results were analyzed according to three damage stages classified by the stress ratio of the specimens. Count index was determined to be the most suitable AE index for evaluating rock mass stability.

• Journal of the Korean Society for Nondestructive Testing
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• v.18 no.2
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• pp.92-102
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• 1998

Prediction of failure behavior in filament-wound composite motor cases is one of the important issues for their reliable application. Acoustic emission during hydroproof testing of the cases is used to solve this problem. Based on the acoustic emission behavior, failure sites can be located successfully. The identification of failure modes is also possible using the distribution of acoustic emission amplitude. Due to the limitation in the number of samples, it is not possible to predict the final burst pressure of motor cases and the effect of impact damage on the final burst pressure.