• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.3
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• pp.254-262
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• 2003

In this study, acoustic emission (AE) signals due to surface cracking and moisture movement in the flat-sawn boards of oak (Quercus Variablilis) during drying under the ambient conditions were analyzed and classified using the principal component analysis. The AE signals corresponding to surface cracking showed higher in peak amplitude and peak frequency, and shorter in rise time than those corresponding to moisture movement. To reduce the multicollinearity among AE features and to extract the significant AE parameters, correlation analysis was performed. Over 99% of the variance of AE parameters could be accounted for by the first to the fourth principal components. The classification feasibility and success rate were investigated in terms of two statistical classifiers having six independent variables (AE parameters) and six principal components. As a result, the statistical classifier having AE parameters showed the success rate of 70.0%. The statistical classifier having principal components showed the success rate of 87.5% which was considerably than that of the statistical classifier having AE parameters.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.3
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• pp.92-100
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• 1995

This experimental study is mainly investigated on the mean cutting forces and AE(acoustic emission) parameters in order to detect and estimate the tool failure in the pachinig of SUS304 by face milling Mean cutting forces and AE parameters can detect the tool failure in face milling. Effective detection parameters are AE RMS, AE energy, AE count, AE duration, and z-direction mean cutting force. From the analysis of cutting tool failure detection, the tool failure of face milling is caused by sudden increasing of the cutting force.

• 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 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.

• Progress in Superconductivity and Cryogenics
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• v.1 no.2
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• pp.8-14
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• 1999

In this study, acoustic emission(AE) technique has been applied to detecting quench which is one of the serious peoblems to assure the integrity of superconducting coil at cryogenic temperature. The characteristics of AE parameters have been analyzed by correlating with the number of quenches, whinding tension of superconducting coil and charge rate of transport current. The quench localization was also performed using AE signals and there was also good correlation between quench current and AE parameters such as AE energy and AE events. In this study, it was confirmed that AE signals were mainly due to the conductor motion which caused by premature quenching. It was also found that optimized winding tension at superconducting coil was needed to prevent quench caused by conductor motion.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2001.05a
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• pp.268-271
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• 2001

When compared to other composite materials such as FRP and MMC, hybrid composite material is more attractive one due to the high specific strength and the resistance to fatigue. However, the fracture mechanism of hybrid composite material is extremely complicated because of the bonding structure of metals and FRP. Recently, nondestructive technique has been used to evaluate the fracture mechanism of these composite materials. In this study, AE technique has been used to clarify the fracture mechanism and the degree of damage for Al 7075/CFRP hybrid composite material. It was found that AE event, energy and amplitude among AE parameters were effective to evaluate fracture process of Al 7075/CFRP composite material. In addition, the relationship between the AE signal and the characteristics of failure surface using optical microscope was discussed.

• Smart Structures and Systems
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• v.19 no.2
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• pp.127-136
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• 2017

Fiber reinforced polymer (FRP) has received widespread attention in the field of civil engineering because of its superior durability and corrosion resistance. This article presents the damage mechanisms of a novelty composite called carbon nanofiber modified flax fiber polymer (CNF-modified FFRP). The ability of acoustic emission (AE) to detect damage evolution for different configurations of specimens under uniaxial tension was examined, and some useful AE characteristic parameters were obtained. Test results shows that the mechanical properties of modified composites are associated with the CNF content and the evenness of CNF dispersed in the epoxy matrix. Various damage mechanisms was established by means of scanning electron microscope images. The fuzzy c-means clustering were proposed to classify AE events into groups representing different generation mechanisms. The classifiers are constructed using the traditional AE features -- six parameters from each burst. Amplitude and peak-frequency were selected as the best cluster-definition features from these AE parameters. After comprehensive comparison, a correlation between these AE events classes and the damage mechanisms observed was proposed.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1572-1579
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• 2006

An usual acoustic emission(AE) event has two widely characterized parameters in time domain, peak amplitude and event duration. But noise in AE measuring may disturb the signals with its parameters and aggrandize the signal incertitude. Experiment activity of detection of the nick inside of porcelain with AE was made and study on AE signal processing with statistic be presented in this paper in order to pick-up information expected from the signal with noise. Effort is concentrated on developing a novel arithmetic to improve extraction of the characteristic from stochastic signal and to enhance the voracity of detection. The main purpose discussed in this paper is to treat with signals on amplitudes with statistic mutuality and power density spectrum in frequency domain, and farther more to select samples for neural networks training by means of least-squares algorithm between real measuring signal and deterministic signals under laboratory condition. By seeking optimization with the algorithm, the parameters representing characteristic of the porcelain object are selected, while the stochastic interfere be weakened, then study for detection on neural networks is developed based on processing above.

• Proceedings of the Korea Institute of Applied Superconductivity and Cryogenics Conference
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• 1999.02a
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• pp.34-38
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• 1999

An acoustic emission(AE) technique has been used to monitor and diagnose quenching phenomenon in racetrack shaped superconducting magnets at cryogenic environment of 4.2K. The ultimate goal is to ensure the safety and reliability of large superconducting magnet systems by being able to identity and locate the sources of quench in superconducting magnets. The characteristics of AE parameters have been analyzed by correlating with quench number, winding tension of superconducting coil and charge rate by transport current. It was found in this study stat there was good correlation between quench current and AE parameters. The source location of quenching in superconducting magnet was also discussed on the correlation between magnet voltage and AE energy.

• Journal of Biosystems Engineering
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• v.29 no.3
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• pp.261-266
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• 2004

This study was Performed to classify the acoustic emission(AE) signal due to surface cracking and moisture movement in the flat-sawn boards of oak(Quercus Variablilis) during drying using the principal component analysis(PCA) and artificial neural network(ANN). To reduce the multicollinearity among AE parameters such as peak amplitude, ring-down count event duration, ring-down count divided by event duration, energy, rise time, and peak amplitude divided by rise time and to extract the significant AE parameters, correlation analysis was performed. Over 96 of the variance of AE parameters could be accounted for by the first and second principal components. An ANN analysis was successfully used to classify the Af signals into two patterns. The ANN classifier based on PCA appeared to be a promising tool to classify the AE signals from wood drying.