• 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.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.3
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• pp.262-267
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• 2007

Detection of gas leak from a tube is a very important issue in the quality control of machines such as the heat exchanger of an air-conditioner, because leakage of operating gas directly reduces the performance of machines. The acoustic emission (AE) method is a common way to detect leak of gas, however its application under the environment of mechanical vibration is restricted since most AE detectors are very sensitive to external vibration noise. In order to overcome this problem, we propose a method based on the mode analysis of the Lamb wave. In this method, the dominant Lamb mode and its frequency are found first, and then a proper band-pass filter is used to retain only this frequency component. In this way, we could improve the SNR (signal-to-noise ratio) of AE signal generated by gas leak from the tube even under vibratory environment.

• Proceedings of the Acoustical Society of Korea Conference
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• 1992.06a
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• pp.87-90
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• 1992

Detecting acoustic emission (AE) provides an appropriate method to diagonize on-line transformers, since acoustic signal is not influenced by strong electric field. Then AE versus AE signal processing method is investigated. But this processing is difficult that decision of starting point of AE wave with acoustic noise. This problem is sloved by correlation which calculate time interval between two signals eactly. This paper presents a technique locating the eact position of the partial discharge (PD) in a power transformer by the correlation of the AE signals from two ultrasonic sensors. And PD position is displayed on monitor. Laboratory tests confirmed that the proposed method can be used for locating the PD in power transformer.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.4
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• pp.72-77
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• 1992

In this study, using in-process tool failure detecting system by AE method in turning machining, we measured AE signal from the tool, and the surface roughness of workpiece and then compared it with tool wear. As a result, we found that tool failure can be predicted by means of surface roughness of the workpiece and it can be predicted more precisely by the arithmetical average roughness (Ra) than by the maximum height of irregularities (Rmax) of the workpiece. Also, we found that we could judge whether it was sudden failure or the wear by means of the shape of AE signal and the range distri- bution of power spectrum frequency when tool danage was happened.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.7
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• pp.1581-1589
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• 1995

The feasibility of using AE for detecting contact and slip between a workpiece and an end effector has been tested. Specifically, the relationship between the contact and slip motion and the characteristics of the AE signal is theoretically and experimentally investigated. The experimental results manifest that the high sensitivity of AE signal to the contact and slip makes it a good alternative as a robot tactile sensor.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2003.11a
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• pp.384-387
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• 2003

The object of the present work is the analysis of head/disk interaction during start/stop and constant speed operation using acoustic emission (AE). The frequency spectrum analysis is performed using the AE signal obtained during the head/disk interaction. The FFT (Fast Fourier Transform) analysis of the AE signals is used to understand the interaction between the AE signal and the state of contact.

• 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 of Manufacturing Technology Engineers
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• v.21 no.4
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• pp.601-607
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• 2012

The magnetic abrasive polishing (MAP), for online monitoring with AE sensor attachment, was performed in this study. To predict the surface roughness after the magnetic abrasive polishing of NAK80, the signal data acquired from the AE sensor were analyzed. A dimensionless coefficient, which consisted of average of AErms and standard deviation of AE signal, was defined as a characteristic of the MAP and a prediction model was obtained using least square method. A neural network, which had multiple input parameters from AE signals and polishing conditions, was applied for predicting the surface roughness. As a result of this study, it was seen that there was very close correlation between the AE signal and the surface roughness in the MAP. And then on-line prediction of the surface roughness after the MAP of the NAK80 was possible by the developed prediction model.

• Tribology and Lubricants
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• v.18 no.5
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• pp.351-356
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• 2002

The surfaces of machine components in sliding contact such as bearing, gears and pistons etc. frequently operate under the condition of mixed lubrication due to high load, high speed and slip. These machine components often undergo the inception of scuffing in practical application. The scuffing failure is a critical problem in modern machine components, especially for the requirement of high efficiency and small size. However, it is difficult to find a universal mechanism to explain all scuffing phenomena because there are so many factors affecting the onset of scuffing. In this study, scuffing experiments are conducted using Acoustic Emission(AE) measurement by an indirect sensing approach to detect scuffing failure. Acoustic Emission(AE) signal has been widely utilized to monitor the interaction at the friction interface. Using AE signals we can get an indication about the state of the friction processes, about the quality of solid and liquid layers on the contacting surfaces in real time. The FFT(Fast Fourier Transform) analyses of the AE signal are sued to understand the interfacial interaction and the relationship between the AE signal and the state of contact is presented.

• 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.