• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.10a
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• pp.331-332
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• 2006

• 한국초전도학회:학술대회논문집
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• v.15
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• pp.64-64
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• 2005

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.5
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• pp.466-472
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• 2009

An acoustic leak monitoring system(ALMS) using acoustic emission(AE) technique was applied for leakage detection of nuclear power plant's pipeline which is operated in high temperature and pressure condition. Since this system only monitors the existence of leak using the root mean square(RMS) value of raw signal from AE sensor, the difficulty occurs when the characteristics of leak size and shape need to be evaluated. In this study, dual monitoring system using AE sensor and accelerometer was introduced in order to solve this problem. In addition, artificial neural network(ANN) with Levenberg.Marquardt(LM) training algorithm was also applied due to rapid training rate and gave the reliable classification performance. The input parameters of this ANN were extracted from varying signal received from experimental conditions such as the fluid pressure inside pipe, the shape and size of the leak area. Additional experiments were also carried out and with different objective which is to study the generation and characteristic of lamb and surface wave according to the pipe thickness.

• Smart Structures and Systems
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• v.12 no.2
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• pp.97-119
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• 2013

This paper presents numerical and experimental results on the use of guided waves for structural health monitoring (SHM) of crack growth during a fatigue test in a thick steel plate used for civil engineering application. Numerical simulation, analytical modeling, and experimental tests are used to prove that piezoelectric wafer active sensor (PWAS) can perform active SHM using guided wave pitch-catch method and passive SHM using acoustic emission (AE). AE simulation was performed with the multi-physic FEM (MP-FEM) approach. The MP-FEM approach permits that the output variables to be expressed directly in electric terms while the two-ways electromechanical conversion is done internally in the MP-FEM formulation. The AE event was simulated as a pulse of defined duration and amplitude. The electrical signal measured at a PWAS receiver was simulated. Experimental tests were performed with PWAS transducers acting as passive receivers of AE signals. An AE source was simulated using 0.5-mm pencil lead breaks. The PWAS transducers were able to pick up AE signal with good strength. Subsequently, PWAS transducers and traditional AE transducer were applied to a 12.7-mm CT specimen subjected to accelerated fatigue testing. Active sensing in pitch catch mode on the CT specimen was applied between the PWAS transducers pairs. Damage indexes were calculated and correlated with actual crack growth. The paper finishes with conclusions and suggestions for further work.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.101-104
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• 1995

A acoustic emission (AE) sensor has been used to monitor tool were during milling process. The relation between tool wear and AE RMS (Root mean Square) signal was investigated experimentally. A avaliable monitoring index for monitoring toolwear was newly extracted form AE RMS. And on-line monitoring program was developed. The proposed monitoring system has verified experimentally by roughing end milling titanium alloy with TIN coated HSS tool.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1991.04a
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• pp.150-157
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• 1991

As the system monitoring technology become required in order to imporve the system performance and the productivity, We've studied to the detection for the tool breakage using AE sensor that is able to detection of generated high frequency strss pulse at cutting. The method of spectrum analysis are used for analysis of AE signals in detection system. The experiments are carred out in a CNC lathe.

• Journal of the Korean Society of Safety
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• v.16 no.4
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• pp.7-13
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• 2001

Wire electrical discharge machining experiments in conducted to investigate characteristics of acoustic emission (AE) and electrical discharge energy due to current peak (I$_{p}$), pulse on time($\tau$/on/). The AE signals are obtained with a sensor attached to workpiece side. Machining states are identified with scanning electron microscopy and residual stress analyzer. It is demonstrated that the residual stress provide reliable informations about the machining states. Moreover, machining states can be detected successfully using both the residual stress and AE count rate.e.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.5
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• pp.9-16
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• 2001

Turning experiments are conducted to investigate characteristics of acoustic emission due to wear of the coated tool. The AE signals are obtained with a sensor attached to tool holder side. Tool states are identified with scanning electron microscopy and optical microscopy. It is demonstrated that the AE signals provide reliable informations about the cutting processes and tool states. Moreover, tool wear can be detected successfully using the AE-RMS.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.1022-1027
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• 1997

The grinding operation is an important machining process for machining of final surface. However. grinding process has inevitable troubles such as loading and glazing for grinding wheel. It is, therefore, an essential research theme to determine the wheel life and the dressing timefor efficient grinding. In this study, AE signals (AEavg) generated in thc grinding operation were measured and the dressing time was determined from the analysis of the AEavg value. To verify the propriety of the obtained result. the AE signals measured on the grinding and the dressing operation were compared with the grinding force signals and the dressing force which were measured at same time. From the obtained result, it was confirmed that the determination of the wheel life and the dressing time by the AE measurement technique proposed in this study can be practically used.

• Proceedings of the KIEE Conference
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• 1999.07e
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• pp.2338-2340
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• 1999

In this paper measurements of AE (Acoustic Emission) signals caused by corona discharge were performed to analyze the electrical deterioration in oil. We also examined the relationship between discharge magnitude and peak-to-peak value of AE signals to diagnose the deterioration of liquid dielectrics. From these results, Vpp(peak to peak value) of AE signals was proportional to corona discharge magnitude. The main frequency band of AE signals in oil appeared to 130[kHz].