• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.3
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• pp.268-274
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• 2004

Patch type piezo-polymer sensors for smart structures were experimented to detect elastic wave. The pencil lead braking test was performed to analyze the characteristics of patch-type piezo-polymer sensors such as polyvinyliden fluoride (PVDF) and polyvinylidene fluoride trifluorethylene (P(VDF-TrFE)) for several test specimens with various elastic wave velocities and acoustical impedances. The characteristics of the patch-type piezo-polymer sensor were compared with the commercial PZT acoustic emission (AE) sensor. The vacuum grease and epoxy resin were used as a couplant for the acoustic impedance matching between the sensor and specimen. The peak amplitude of elastic wave increased as the diameter of piezo-film and acoustical impedance of the specimen increased. The frequency detection range of the piezo-film sensors decreased with increasing diameter of the piezo-film sensor. The P(VDF-TrFE) sensor was more sensitive than the PVDF sensor.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.223-226
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• 2003

This paper presents a realization of sensor signal processing(noise filtering) and Fieldbus based communication for condition monitoring of check valve. we first acquired the AE(Acoustic Emission) sensor data at the KAERI check valve test loop, and their frequencies were analyzed to find the informative band. To reject background noises, bandpass filters have been designed. Also, to send the processed data to a remote site, wired communication facility has been realized via DeviceNet.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.315-318
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• 1997

In terms of productivity, the speed of machining process has been increasing in most of engineering part. But the tapping process does not reach at enough level compared with other machining processes because of its complicate cutting mechanism. In the high speed tapping process, the one of important elements is tool monitoring system to prevent tool breakage. This paper describes tool monitoring system by acoustic emission(AE) in the tapping process. We used 2 types of AE sensors in this test. The one is commercial sensor which is used in other machining monitoring system like polishing and the other is a self-fabricated sensor for this test. In this test we purpose to find out the frequency of AE signal in tapping process and verify the possibility of applying AE sensor in in-process tapping monitoring system. Also grasp of characteristic of tapping process by AE signal is handled.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.11
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• pp.93-101
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• 1997

Ever since the nonlinearity of machine tool dynamics was established, researchers attempted to make use of this fact to devise better monitoring, diagnostics and control system, which were hitherto based on linear models. Theory of chaos which explains many nonlinear phenomena comes handy for furthering the analysis using nonlinear model. In this study, measuring system will be constructed using multi-sensor (Tool Dynamometer, Acoustic Emission) in end milling process. Then, it will be verified that cutting force is low-dimensional chaos by calculating Lyapunov exponents. Fractal dimension, embedding dimension. And it will be investigated that the relation between characteristic parameter calculated from sensor signal and tool wear.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.3
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• pp.14-21
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• 1998

The in-process detection of the state of cutting tool is one of the most important technical problem in Intelligent Machining System. This paper presents a method of detecting the state of cutting tool in turning process, by using Artificial Neural Network. In order to sense the state of cutting tool. the sensor fusion of an acoustic emission sensor and a force sensor is applied in this paper. It is shown that AErms and three directional dynamic mean cutting forces are sensitive to the tool wear. Therefore the six pattern features that is, the four sensory signal features and two cutting conditions are selected for the monitoring system with Artificial Neural Network. The proposed monitoring system shows a good recogniton rate for the different cutting conditions.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.12
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• pp.100-108
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• 1995

The In-process minitoring of the chatter vibration is necessarily required to an automatic manufacturing system. In this study, we constructed a multi-sensing system using tool dynamoneter, accelerometer and AE(Acoustic Emission) sensor for a more credible detection of chatter vibration. And a new approach using a multiple neural network to extract the features of multi-sensor for the recognition chatter vibration is proposed. With the Back-propagation training process, the neural network memorize and classify the features of multi-sensor signals. As a result, it is shown by multiple neural network that the chatter vibration can be monitored accurately, and it can be widely used in practical unmanned system.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.29 no.8
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• pp.466-469
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• 2016

In this study, in order to develop composition ceramics for Acoustic Emission (abbreviated as AE) sensor application, the PZT system ceramics was fabricated by conventional solid state reaction method. When x=0.48, the density, electromechanical coupling factor($k_p$), piezoelectric coefficient $d_{33}$ and piezoelectric voltage constant $g_{33}$ of the maximum values of $7.857g/cm^3$, 0.51, 190[pC/N], 52[$10^{-3}mV/N$] were obtained, respectively, suitable for AE sensor.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.7
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• pp.891-897
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• 2013

In this study, the compressive deformation behaviors of aluminum alloy under high strain rates were investigated by means of a SHPB test. An acoustic emission (AE) technique was also employed to monitor the signals detected from the deformation during the entire impact by using an AE sensor connected to the specimen with a waveguide in real time. AE signals were analyzed in terms of AE amplitude, AE energy and peak frequency. The impacted specimen surface and side area were observed after the test to identify the particular features in the AE signal corresponding to the specific types of damage mechanisms. As the strain increased, the AE amplitude and AE energy increased whereas the AE peak frequency decreased. It was elucidated that each AE signal was closely associated with the specific damage mechanism in the material.

• Composites Research
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• v.14 no.5
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• pp.46-53
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• 2001

In this research, damage behavior of singly oriented ply (SOP) fiber metal laminate (FML) subjected to concentrated load was studied. The static indentation tests were conducted to study fiber orientation effect on damage behavior of FML. During the static indentation tests, acoustic emission technique (AE) was adopted to study damage characteristics of FML. AE signals were obtained by using AE sensor with 150kHz resonance frequency and the signals were compared with indentation curves of FML. The damage process of SOP FML was divided by three parts, i.e., crack initiation, crack propagation, and penetration. The AE characteristics during crack initiation show that the micro crack is initiated at lower ply of the plate, then propagate along the thickness of the plate with creating tiber debonding. The crack grow along the fiber direction with occurring 60∼80dB AE signal. During the penetration, the fiber breakage was observed. As fiber orientation increases, talc fiber breakage occurs more frequently. The AE signal behaviors support these results. Cumulative AE counts could well predict crack initiation and crack propagation and AE amplitude were useful for the prediction of damage failure mode.

• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.1
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• pp.68-76
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• 2011

Acoustic emission(AE) has emerged as a powerful nondestructive tool to detect any further growth or expansion of preexisting defects or to characterize failure mechanisms. Recently, this kind of technique, that is an in-situ monitoring of inside damages of materials or structures, becomes increasingly popular for monitoring the integrity of large structures like a huge wind turbine blade. Therefore, it is required to find a symptom of damage propagation before catastrophic failure through a continuous monitoring. In this study, a new damage location method has been proposed by using signal mapping algorithm, and an experimental verification is conducted by using small wind turbine blade specimen; a part of 750 kW real blade. The results show that this new signal mapping method has high advantages such as a flexibility for sensor location, improved accuracy, high detectability. The newly proposed method was compared with traditional AE source location method based on arrival time difference.