• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.678-682
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• 1996

Acoustic emission(AE) is monitored during end milling with a sensor attached to the cutting fluid discharge nozzle. Cutting forces are also measured and compared with the AE signals to examine the reliability of the AE signals. It is demonstrated that the AE signals provide reliable informations about the cutting processes and tool states. Moreover, edge chipping and fracture of tools can be successfully detected using both the AE signals and cutting forces.

• 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.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.5
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• pp.480-487
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• 2003

To improve environmental control in various plants, signal characteristics of plants have been studied by a nondestructive technique. In this paper, the acoustic emission (AE) from plants was analyzed for water stress dependency. AE signals were taken from gymnosperm and angiosperm. AE sensor detected AE signals from the plant stem underneath the plant surface below the sensor. AE hit-event counts in daytime were more than those in night time, and it was found that the daily hit counts pattern was strongly affected by the water stress in the plant. frequency bands of AE signals from the angiosperm was different from those from the gymnosperm. Frequency bands of AE in outdoor condition were in accord with those in indoor having similar conditions.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.6 s.171
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• pp.109-117
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• 2005

In case of fine machining processes, the cutting state monitoring by a skilled operator is impossible because the physical changes generated during fine machining are very weak. To realize the high efficient and precise fine machining, it is necessary to develop the sensor based monitoring system which is able to detect the fine changes of cutting state. In this paper, the fine acoustic emission monitoring system is developed to monitor the state of the fine machining process. The developed system consists of the AE sensor and the AE signal processing unit. And this has the high-sensitivity and bandwidth which can detect fine AE signal generated during fine machining process. In order to investigate the feasibility of the developed system, evaluation experiments were performed in the fine fixed-abrasive machining processes such as polishing and glass ferrule slicing. Experimental results show that the developed monitoring system possesses an excellent real-time monitoring capability at fine machining processes.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1999.06a
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• pp.215-219
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• 1999

One of the innovative physical methods that provide insight into the basic processes which determine friction and wear behavior of coated machine tools is acoustic emission (AE). In this study, an investigation of the relation between AE and friction signal produced during repeated sliding test is presented. The material of test specimens is CrN coated 0.2% plain carbon steel with 1 Um thickness. The obtained results demonstrate that AE signal is very related with friction, and AE signal is more sensitive than friction when CrN coated film come off the substrate.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1999.06a
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• pp.170-174
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• 1999

In order to analysis contact mechanism of head/ disk interface on hard disk drive, many techniques of analysis have been developed. Acoustic Emission sensor can be used for detect elastic energy of contact at head/disk interface. This work presents a good understanding of slider characteristics in frequency domain. The results show that torsional and bending mode of slider are dominant in head/disk contact.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.921-924
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• 2004

This paper is to investigate a crack for plasma sprayed MCrAlY coated material by acoustic emission method in 4-point bending test. The CoNiCrAlY is coated on Inconel-718 by vacuum plasma spray process. Micro-hardness measurement was conducted by means of Micro Vickers-hardness indentor. The porosity of coating layer was measured using a SEM and Image Analyzer. AE monitoring system is composed of PICO type sensor, a wide band preamplifier(40dB), a PC and AE DSP(16/32 PAC) board. The AE count, Hit and energy of coating specimens is measured according to coating thickness.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.4
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• pp.95-99
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• 2004

In this study, the leak detection technique of pressure vessel by using acoustic emission(AE) signal is suggested experimentally. The leak of pressure vessel is located at the welding line due to welding defects. we measured the AE signal using Rl5I sensor, and examined the AE parameters in leak condition. It is investigated that the mean value of AE signal is dependent on leak source location. So the absolute mean value of AE signal is adopted as dominant AE parameter. We proposed leak detection algorithm using AE signal mean value for monitoring the leak source location.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.905-908
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• 2007

The purpose of this work is to obtain fundamental data about fatigue crack detection of the welded exhaust flange by using the AE method. The acoustic emission method as a nondestructive evaluation is one of high technical test for realtime monitoring in the dangerous industry fields. Signal analysis of both AE sensor and accelerometer for fatigue crack failure are presented in this paper.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.2
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• pp.34-39
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• 2012

In this study, the friction welding experiment was performed by using the design of experiment. And the signal data acquired by acoustic emission sensor were analyzed to predict the tensile strength of friction welding part at friction welding process for AZ31 magnesium alloy. A dimensionless coefficient($\phi_{AE}$), which consisted in the square of AE rms and variance, was defined as the characteristic of friction welding and the prediction equation was obtained by using linear regression. As the result of analysis, it was seen that the correlation between predicted and measured values became very close and on-line prediction of the ensile strength was possible in friction welding part.