• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.249-255
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• 2020

This study developed the health monitoring method of press process using the acoustic emission (AE) sensor and high-pass filter. Also, the AE parameters such as ring-down count and peak amplitude are used. Based on this AE signal, the AE parameters were acquired and was utilized to detect the crack of the specimen. Since the defect detection is difficult due to noise and low magnitude of signal, the signal noise and press operation frequency were checked through the Short Time Fourier Transform(STFT) and damped. High-pass Filtering data was applied to AE parameters to select effective parameters. By using this signal processing techniques, the proposed AE parameters could improve the performance of defect detection in the press process.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.3
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• pp.271-279
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• 2006

Acoustic Emission (AE) technology is an effective nondestructive testing for continuous monitoring of defect formation and failures in structural materials. This paper presents a source location model using Acoustic Emission (AE) sensors in a Pre-Stressed Concrete (PSC) beam and the evaluation of the model was performed through lab experiments. 54 AE events were made on the surface of the 5m-PSC beam using a Schmidt Hammer and arrival times were measured with 7AE sensors. The source location f3r each event was estimated using least squares. The results were compared with actual positions and the RMSE (Root Mean Square Errors) was about 2cm.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.6
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• pp.48-57
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• 1998

Abrasive waterjet(AWJ)은 가공시 열에 의한 가공경화가 없기 때문에 유리, 세라믹, 타이타늄및 금속복합재료와 같은 난삭재의 가공기술로 사용이 증가되었다. Acoustic emission(AE)신호에 의한 AWJ 세라믹 drilling가공시 On-Line Monitoring의 가능성이 고찰되었다. 기계 적인 물성이 서로 상이한 3종류의 세라믹이 본 연구에서 사용되었으며, AE신호는 AWJ drilling의 깊이를 monitoring하는데 유용함을 알 수 있었고 또한 세라믹의 material removal mechanisms을 규명하였다.

• Journal of the Korean Society for Nondestructive Testing
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• v.20 no.6
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• pp.490-500
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• 2000

A new technique for the source location of acoustic emission (AE) in plates whose thichness are close to or thinner than the wavelength has been studied by introducing wavelet transform de-noising technique. The detected AE signals were pre-processed using wavelet transform to be decomposed into the low-frequency, high-amplitude flexural components and the high-frequency, low-amplitude extensional components. If the wavelet transform de-noising was employed, we could successfully filter out the extensional wave component, one of the critical errors of source location in plates by arrival time difference method. The accuracy of source location appeared to be significantly improved and independent of the setting of gain and threshold, plate thickness, sensor-to-sensor distance, and the relative position of source to sensors. Since the method utilizes the flexural component of relatively high amplitude, it could be applied to very large, thin-walled structures in practice.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.419-422
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• 1995

In this paper, we propose two methods for AE source location on the material with unknown AE wave velocity. By this method, we can apply this method to arbitrary material of which properties are not known exactly. Also, in this paper, the mechanism of error generation in both methods are discussed and performances are compared by using computer simulation and experiments which uses a lead break as the AE source on the aluminum plate.

• Magazine of the Korea Concrete Institute
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• v.10 no.6
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• pp.203-211
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• 1998

It is well recognized recently that acoustic emission, which is an elastic wave generated from rapid release of elastic energy in steressed solids, is very useful tool for on-line monitoring of microscopic behavior of deformation of material. In this study, three point bend test was performed to evaluate the microscopic damage progress during the loading and failure mechanism of mortar beam by monitoring the characteristic of AE signal. The relationship between AE characteristic and microscopic failure mechanism is discussed. In addition 2 dimensional AE source location based on triangular method was also done to monitor the intiation and propagation of micro crack around notch tip of mortar beam. It was shown that AE source location was very effective to predict the growth behavior of micro crack in mortar beam specimen.

• Journal of the Korean Society for Nondestructive Testing
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• v.18 no.3
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• pp.191-204
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• 1998

One of the key issues in acoustic emission (AE) during hydroproof test of filament-wound composite rocket motor cases is the determination of the optimal component of elastic wave to be monitored. To solve this problem, broadband ultrasonic wave was generated into a symmetrically filament-wound composite motor case, and was received at 105 different locations after the propagation through the composite case with different distances and directions. By analysis of the received signals, characteristics of elastic wave propagation such as frequency components, the maximum propagating distance, and velocity surface were investigated. This analysis was performed for two different conditions of the motor case; air-filled and hydraulically pressurized. Based on these information, the effect of hydraulic pressure on the wave propagation characteristics was investigated and furthermore, the optimal component of elastic wave for AE during hydroproof test of the motor case was successfully determined.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.5
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• pp.465-475
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• 2004

This paper is the first part of the study on the development of a smart active layer (SAL) sensor, which consists of two parts. In this first part, the theory and concept of the SAL sensor is investigated, which is designed for the detection of elastic waves caused by internal cracks and damages in structures. For the development SAL sensor, (i) the basic theory of elastic waves was studied, (ii) the feasible study of the SAL as an elastic waves detection sensor using the finite element analysis (FEA) with respect to a piezoceramic disc was performed. (iii) the comparison of performances between some piezoceramic sensors and a commercial acoustic emission (AE) sensor was accomplished to ensure the applicability by the experimental means, such as a pencil lead break test. Also, the conceptional study for the SAL sensor, which can be utilized for the effective detection and locating of defects by the arrangement of regularly distributed sensors, was discussed.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.1
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• pp.45-51
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• 2004

Boiler tubes in power plants are often leaked due to various material degradations including creep and thermal fatigue damage under severe operating conditions such as high temperature and high pressure over an extended period of time. To monitor and diagnose the tubes on site and in real time, the acoustic emission (AE) technology was applied. We developed an AE leak detection system, and used it to study the variation of AE signal from the on-site tubes in response to the changes in the boiler operation condition and to detect the locations of leakage based on it. Detection of leak was performed by acquiring and evaluating the signals in separate regimes of high and low frequency signal. As a result of these studies, we found that on-line monitoring and detection of leak location for boiler tubes is possible using the developed system. Thus, the system is expected to contribute to the safe operation of power plants, and prevent economic losses due to potential leak.

• 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.