• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• 제14권6호
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• pp.35-42
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• 2000

In this paper, AE(Acoustic Emission) signals detected from the growth of the electrical tree in an epoxy resin under ac high-voltage application were analysed to diagnose the insulation deterioration of cast-resin power transformer. Frequency spectra of AE signals generated from the magnetizing and the load currents in the actual operating cast-resin power transformer of 500[kVA] under distribution system of 22.9[kV] were also analysed to distinguish the AE signals due to void discharges from the magnetic circuit noises in the core of the transformer. As the experimental results, we could distinguish the AE signals whether those signals were caused due to the void discharges or due to the magnetic circuit noises by analyzing the frequency spectrum of AE signals. The frequency spectra of AE signals generated from the cast-resin power transformer in operation due to both the magnetizing and the load currents appeared in the range of 40-120[kHz], but the frequency band of AE signals emitted from the void discharges in an epoxy resin sample was about 50[kHz] to 230[kHz].

• Journal of the Korean Society of Manufacturing Process Engineers
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• 제3권1호
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• pp.28-37
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• 2004

An on-line monitoring system of endmill failure such as weal, chipping, and fracture is developed using AE, cutting force Characteristic variations of AE and cutting force signals due to endmill failure are identified as follows. When endmill fracture occurs, AE count rate shows a rapid Increase in conjunction with a subsequent decrease while a standard deviation of the principal cutting force Increases significantly. The increase of AE count rate precedes the Increase of standard deviation of principal cutting force. Chipping results in relatively small increase and decrease of AE count rate without any significant variation of the cutting force Gradual increase of AE count rate and mean principal cutting force are Identified to be related with the wear of cutter. A cutter fracture detection algorithm is developed based on the present results. The signals me normalized to enhance the applicability of the algorithm to Wide those of fresh cutters, and qualitative characteristics of AE signals encountered at the moment of fracture are employed. It is demonstrated that the algorithm can detect the cutter fracture successfully.

• Transactions of the Korean Society of Mechanical Engineers
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• 제12권5호
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• pp.1035-1042
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• 1988

The acoustic emission(AE) signal is monitored in milling operation in order to investigate the relationship between the progressive tool wear and the AE signals. A signal processing technique so called time domain averaging(TDA) is presented for the elimination of the influences of the noise imbedded in the periodic signals. The relationship between the progressive tool wear and the AE signals is investigated by varying the cutting speed, feed, depth of cut and the number of insert. From the measured data, it is observed that the averaged level of the AE signal increases at first with the increase of flank wear to a certain critical value, and then stays almost constant or fluctuates with further increase of the flank wear.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 한국해양공학회 2002년도 춘계학술대회 논문집
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• pp.130-133
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• 2002

Polyethylene pressure pipe has been used with a gas pipe material because of workability and stability etc.. Researches on characteristics of polyethylene pressure pipe are carried out, but there are rare. In this study, the tensile test was performed on polyethylene pipe. From the tensile test, AE signals were detected and estimated in real time. Also, the time-frequency analysis of AE signals was analyzed. From test results, PE pipes were displayed typical stress-strain curves oj semi-crystalline polymer. As result analyzed AE signals, could divide stress-strain curves could be divided into four stages. In the elastic region, signals were not detected. Low amplitude distributions of 30-35dB appeared after yielding, and high amplitude distributions of 30-60dB appeared with increased extension. From the time-frequency analysis of AE signals, the frequency band of 100kHz appeared mainly. Also, the frequency band of 300kHz appeared before the necking phenomenon spreads into the whole region, and the frequency band of 500kHz appeared on extension earlier.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• 제23권4호
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• pp.330-336
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• 2014

The continuous chip generated in cutting process deteriorates workpiece, tool, and machine tool system. It is necessary to treat this continuous chip in ductile material machining condition for stable cutting. This paper deals with the chip control method using acoustic emission(AE) signal in pure copper turning operation. AE raw signals, root mean square(RMS) signals and wavelet transformed signals measured in turning process are introduced to analysis for chip patterns. With analysis of AE signals, it is obtained that the produced chip patterns are correlated with the specified AE signals which are transformed by fuzzy pattern algorithm. By this experimental investigation, the chip patterns can be classified at significant level in pure copper machining process and controlled from continuous chips to reduced-length stable chips.

• Composites Research
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• 제16권6호
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• pp.16-22
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• 2003

This paper studied the AE(acoustic emission) characteristics of the laminated composite structures. The composite stiffened panels under the compressive loading emitted various AE signals when they buckled or changed the buckling modes. In addition, the failure initiated and propagation generated a lot of complex signals. From the continuous signal generation. we identified when the failures initiated and whether they propagated or not. The single lap joint of laminated plates under tensional load also generated AE signals when bonding region failed. The first failure occurrence and its propagation are monitored by generated AE signals. The characteristics of AE signals used in this analysis are cumulative hits, hit distribution, peak frequency of generated AE waveform and amplitude of signals. The analysis of AE signals shows that continuous increment of cumulative hits can be regarded as damage propagation and three dominant peak frequencies can correspond to typical failure modes in the laminated composites.

• Journal of the Korean Society for Precision Engineering
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• 제13권4호
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• pp.136-142
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• 1996

Automatic monitoring of cutting process is one of the most important technology in machining. AE sensing technology has been applied to monitoring process and proved to be effective in detecting tool abnor- malities such as tool wear and fracture. In this experimental study. AE signals were detected from the tool holder for continuous and interrupted cutting, which obtained from changing workpice material configuration, under control of constant cutting speed from CNC lathe. From statistical and frequency analysis, the AE signals were analyzed to obtaining the characteristics of continuous and interrupted cutting conditions and tool failure. The Kurtosis values decreased but RMS voltages increased as the cutting speed increased, in both continuous and interrupted cutting. RMS voltage is suddenly increased but Kurtosis value is suddenly decreased when tool failure condition. Power spectrum density of AE signals when tool failure reaches extreme value around 0.065 cycles/ .mu. m.

• Geomechanics and Engineering
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• 제14권2호
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• pp.107-113
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• 2018

Coal samples with different joints morphology were subjected to uniaxial compression experiments, cracks evolution was recorded by Nikon D5300 and acoustic emission (AE) energy signals were collected by AEwin Test for Express-8.0. During loading process, coal samples deformed elastically with no obvious cracks changes, then they expanded gradually along the trace of the original cracks, accompanied by the formation of secondary cracks, and eventually produced a large-scale fracture. It was more interesting that the failure mode of samples were all shear shape, whatever the original cracks morphology was. With cracks and damage evolution, AE energy radiated regularly. At the early loading stage, micro damage and small scale fracture events only induced a few AE events with less energy, while large scale fracture leaded to a number of AE events with more energy at the later stage. Based on the multifractal theory, the multifractal spectrum could explain AE energy signals frequency responses and the causes of AE events with load. Multifractal spectrum width (${\Delta}{\alpha}$), could reflect the differences between the large and small AE energy signals. And another parameter (${\Delta}f$) could reflect the relationship between the frequency of the least and greatest signals in the AE energy time series. This research is helpful for us to understand cracks evolution and AE energy signals causes.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• 제7권6호
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• pp.64-71
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• 1998

This paper investigates the relationship between cutting conditions and Acoustic Emission(AE) signals; $AE_{avg}$, $AE_{rms}$, $AE_{mode}$$AE_{avg}$ and $AE_{rms}$ are increased as the increasing of cutting velocity and depth of cut respectively. The new parameters, derived from $AE_{avg}$ and $AE_{rms}$, which may be used for the in-process detection of tool wear is discussed. It is also known that $AE_{mode}$

• Structural Engineering and Mechanics
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• 제67권5호
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• pp.545-553
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• 2018

Mode II delamination propagation is an important damage mode in laminated composites and this paper aims to investigate the behavior of this damage in laminated composite materials using acoustic emission (AE) technique. Three different lay-ups of glass/epoxy composites were subjected to mode II delamination propagation and generated AE signals were recorded. In order to investigate the propagation of delamination behavior of these specimens, AE signals were analyzed using Wavelet Packet Transforms (WPT) and Fast Fourier Transform (FFT). In addition, conventional AE analyses were used to enhance understanding of the propagation of delamination damage. The results indicate that different fracture mechanisms were the main cause of the AE signals. The dominant mechanisms in all the specimens were matrix cracking, fiber/matrix debonding and fiber breakage, with varying percentage of the damage mechanisms for each lay-up. Scanning Electron Microscopy (SEM) observations were in accordance to the AE results.