• Journal of the Korean Society of Manufacturing Process Engineers
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• v.9 no.4
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• pp.14-25
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• 2010

Small cracks or chips of a product appear very frequently in the course of continuous production of an automatic press process system. These phenomena became the cause of not only defective product but also damage of a press mold. In order to solve this problem AE(Acoustic emission) system was introduced. AE system was expected to be very effective to real time detection of the defective product and for the prevention of the damage in the press molds In this study, for the pick and analysis of AE signals generated from the press process, AE sensors/pre-amplifier/analysis and processing board were used as frequently found in the other similar cases. For the analysis and processing the AE signals picked in real time from the normal or the detective products, specialized software called AE-win(software for processing AE signal from Physical Acoustics Corporation) was used. As a result of this work it was conformed that intensity and shape of the various AE signals differ depending on the weight of the press and thickness of sheet and process type.

• Proceedings of the KIEE Conference
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• 2000.07c
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• pp.1936-1938
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• 2000

In this paper, a frequency spectra of AE (acoustic emission) signals detected from the partial discharges of an epoxy resin void and a cast resin transformer in operating were analyzed to offer the proper frequency range of AE signals from the corona discharge for the purpose of AE sensor selection, From these results, a frequency spectra of AE signals emitted from the corona discharges in the void of an epoxy resin sample were about 190[kHz] to 220[kHz] by the FFT(fast fourier transform), A frequency spectra of AE signals emitted from a cast. resin transformer with non-load were appeared to be downward of about 140[kHz] by the FFT, and then a frequency spectra of AE signals emitted from the above of cast resin transformer with load were appeared to increase from about 190[kHz] to 220[kHz] by the FFT.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.1
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• pp.54-61
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• 2001

A software package to classify acoustic emission (AE) signals using the wavelet transform and the neural network was developed Both of the continuous and the discrete wavelet transforms are considered, and the error back-propagation neural network is adopted as m artificial neural network algorithm. The signals acquired during the 3-point bending test of specimens which have artificial defects on weld zone are used for the classification of the defects. Features are extracted from the time-frequency plane which is the result of the wavelet transform of signals, and the neural network classifier is tamed using the extracted features to classify the signals. It has been shown that the developed software package is useful to classify AE signals. The difference between the classification results by the continuous and the discrete wavelet transforms is also discussed.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.5
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• pp.429-438
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• 2003

The effects of fiber orientation on acoustic emission(AE) characteristics have been studied for the unidirectional and satin-weave, continuous glass-fiber reinforced plastic(UD-GFRP and SW-GFRP) tensile specimens. Reflection and transmission optical microscopy was used for investigation of the damage zone of specimens. AE signals were classified as different types by using short time fourier transform(STFT) : AE signals with high intensity and high frequency band were due to fiber fracture, while weak AE signals with low frequency band were due to matrix and interfacial cracking. The feature in the rate of hit-events having high amplitudes showed a process of fiber breakages, which expressed the characteristic fracture processes of individual fiber-reinforced plastics with different fiber orientations and with different notching directions. As a consequence, the fracture behavior of the continuous GFRP could be monitored as nondestructive evaluation(NDE) through the AE technique.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.2
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• pp.75-80
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• 2006

In this paper, to detect and observation the void discharges pulse signal, AE signals and tree growth characteristics in case the high voltage is applied to a XLPE sample for a power cable. We also examined the partial discharge current pulse and AE signals with the increase of the applied voltage in XLPE insulation. The experimental results show that a branch-type tree grows in the presence of the voids, and a bush-type tree grows in the absence of the voids in both samples. A rate of tree growth increases abruptly in proportional to the deterioration time in the presence of the of the voids, but in the absence of the voids, a rate of tree growth decreases as time goes by and finally a breakdown occurs. The frequency band of AE signals that are generated from the partial discharges in a XLPE sample, one of solid dielectric materials, is about 1.0[MHz].

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.8
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• pp.1189-1196
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• 2001

The objective of this paper is to investigate the applicability of acoustic emission(AE) technique to monitor the progress of the thermal shock damage on alumina ceramic. For this purpose, alumina ceramic specimen was heated in the furnace and then was quenched in the water tank. When the specimen was quenched in the water tank, complex AE signals due to the initiation of micro-cracks and boiling effect were generated by the progress of thermal shock damage. These mixed AE signals have to be classified for monitoring the degree of the thermal shock damage of alumina ceramics. In this paper, the mixed AE signals generated from both the boiling effect and the crack initiation under thermal shock test was analyzed. The characteristics of AE signals were also discussed by considering the variation of bending strength and Yongs modulus of specimens.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.5
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• pp.514-520
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• 2009

In configuring an automated polishing system, a monitoring scheme to estimate the surface roughness is necessary. In this study, a precision polishing process, magnetic abrasive finishing (MAF), along with an in-process monitoring setup was investigated. A magnetic tooling is connected to a CNC machining to polish the surface of stavax(S136) die steel workpieces. During finishing experiments, both AE signals and force signals were sampled and analysed. The finishing results show that MAF has nano scale finishing capability (upto 8nm in surface roughness) and the sensor signals have strong correlations with the parameters such as gap between the tool and workpiece, feed rate and abrasive size. In addition, the signals were utilized as the input parameters of artificial neural networks to predict generated surface roughness. Among the three networks constructed -AE rms input, force input, AE+force input- the ANN with sensor fusion (AE+force) produced most stable results. From above, it has been shown that the proposed sensor fusion scheme is appropriate for the monitoring and prediction of the nano scale precision finishing process.

• 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.7 no.6
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• pp.90-96
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• 1998

The ductile cast iron is austempered at 300, 350 and 40$0^{\circ}C$ temperature in order to investigate the basic factors for monitoring drill wear in automatic production process, and cutting force and AE RMS signals are measured with changing cutting condition for ADI(Austempered Ductile Cast Iron) with different mechanical properties. The signals of cutting force were influenced by cutting speed and feedrate greatly. On the other hand AE RMS signals are influenced by cutting speed where as it is not related with feedrate. As the depth of drilling increases, cutting force shows a slow increase and the value of AE RMS increases until the range of h/d=4. But over the range it increases greatly due to an amount of chip discharge and friction with inner wall of drilling hole, etc. As the drill diameter increases at a constant depth of drilling. Cutting force increases linearly, but the level of AE RMS does not increases linearly due to circumferential velocity and great influence of h/d.

• Journal of Ocean Engineering and Technology
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• v.17 no.3 s.52
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• pp.46-51
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• 2003

Acoustic emission (AE) signals, emanated during local failure of aluminum alloys, have been the subject of numerous investigations. It is well known that the characteristics of AE are strongly influenced by the previous thermal and mechanical treatment of the sample. Possible sources of AE during deformation have been suggested as the avalanche motion of dislocations, fracture of brittle particles, and debonding of these particles from the alloy matrix. The goal of the present study is to determine if AE occurring as the result of fatigue crack propagation could be evaluated by the joint time-frequency analysis method, short time Fourier transform (STFT), and Wigner-Ville distribution (WVD). The time-frequency analysis methods can be used to analyze non-stationary AE more effectively than conventional techniques. STFT is more effective than WVD in analyzing AE signals. Noise and frequency characteristics of crack openings and closures could be separated using STFT. The influence of various fatigue parameters on the frequency characteristics of AE signals was investigated.