• 한국공작기계학회논문집
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• 제13권5호
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• pp.87-95
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• 2004

From the results of AE(Acoustic Emission) source location occurred by the spot exciting as suggested in this research, it has been confirmed that AE technique is quite fruitful in figuring out the location of the occurrence, form, size and direction of the defects. As the results of examining the distribution of event for the angle of crack $\alpha$ to Xs and Ys, as the increases from $0^{\circ}$ ~ $90^{\circ}$, gradually changes its width from the axis Xs to the axis Ys. So event appears approximately similar in its size at the angle of crack $\alpha$=$45^{\circ}$, yet opposite when $\alpha$ is lager. It is believed that this is a phenomenon where its crack legnth $\alpha$, assumed as a planar defect, is to be prcjected toward the direction with a larger size. Thus, it is expected that the application of the experimental method suggested in this study would make it possible to identify the location of the defect in the material in the nondestructive way.

• 대한기계학회논문집A
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• 제32권10호
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• pp.856-865
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• 2008

Damage mechanisms in bending piezoelectric actuators under electric fatigue loading are addressed in this work with the aid of an acoustic emission (AE) technique. Electric cyclic fatigue tests have been performed up to $10^7$ cycles on the fabricated bending piezoelectric actuators. An applied electric loading range is from -6 kV/cm to +6 kV/cm, which is below the coercive field strength of the PZT ceramic. To confirm the fatigue damage onset and its pathway, the source location and distributions of the AE behavior in terms of count rate and amplitude are analyzed over the fatigue range. It is concluded that electric cyclic loading leads to fatigue damages such as transgranular damages and intergranular cracking in the surface of the PZT ceramic layer, and intergranular cracking even develops into the PZ inner layer, thereby degrading the displacement performance. However, this fatigue damage and cracking do not cause the final failure of the bending piezoelectric actuator loaded up to $10^7$ cycles. Investigations of the AE behavior and the linear AE source location reveal that the onset time of the fatigue damage varies considerably depending on the existence of a glass-epoxy protecting layer.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2005년도 춘계학술대회 논문집
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• pp.162-167
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• 2005

Recently, the advanced industries using micro parts are rapidly growing. Micro endmilling is one of the prominent technology that has wide spectrum of application field ranging from macro to micro parts. Also, the method of micro-grooving using micro endmilling is used widely owing to many merit, but has problems of precision and quality of products due to tool wear and tool fracture. This study deals with condition monitoring using acoustic emission(AE) signal in the micro-grooving. First, the feature extraction of AE signal directly related to machining process is executed. Then, the distinctive micro endmill state according to the each tool condition is classified by using the fuzzy C-means algorithm, which is one of the methods to recognize data patterns. These result is effective monitoring method of micro endmill state by the AE sensing techniques which can be expected to be applicable to micro machining processes in the future.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2004년도 하계학술대회 논문집 Vol.5 No.1
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• pp.515-518
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• 2004

The Piezoelectric ceramics for AE(Acoustic Emission) sensor are desired large electromechanical coupling factor, high mechanical quality factor and good characteristic resonance frequency. In this study, the empirical formula of specimens is used $0.9Pb(Zr_xTi_{1-x})O_3-0.1Pb(Mn_{1/3}Nb_{1/3}Sb_{1/3})O_3$ (PZT-PMNS). The piezoelectric and dielectric characteristic are investigated by sintering temperature and value of x as functions of $Ti^{2+},\;Zi^{2+}$ mol rate. MPB(morphotropic Phase boundary) is defined in the x=0.522. Because it is appeared to the best piezoelectric and dielectric characteristic in the x=0.522, it can be application by AE sensor. PZT-PMNS ceramics without pre-amplifier and filter are tested for detecting of arc signal. The detection characteristic is evaluated wave form, frequency distribution.

• 지질공학
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• 제6권2호
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• pp.59-64
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• 1996

암석 시료가 파괴될 때에 발생되는 AE신호는 미세한 균열 발생시의 갑작스런 변형에너지 해바에 기인한다. 압전 압력형 탐촉자와 다채널 기록장치를 이용하여 AE 신호파를 기록하여 분석하므로 외적인 하중조건과 그에 따른 미세균열의 특징에 대하여 연구하였다. 연구결과 미세균열의 체적은 수 $\mu\textrm{m}^3$ 내지 $150,000\mu\textrm{m}^3$로 산출되어서 그 크기가 넓은 범위로 분포하였고 인장형 미세균열이 대체적으로 전단형 보다 큰 체적을 보였다. 또한, 균열원에서의 에너지 강도는 모드 I 하중조건하에서 발생하는 AE 신호가 혼합모드 조건하에서 발생한 신호보다 약 3배정도 크게 나타났으나,시료가 파괴되는 동안 기록된 AE 신호의 숫자는 반대로 모드 I의 경우가 혼합모드의 25%에 불과하였다. 이러한 사실은 같은 크기의 파괴면을 형성하는데 필요한 에너지 요구량이 대체적으로 동일함을 암시하는 것으로 보인다.

• 비파괴검사학회지
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• 제15권4호
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• pp.520-530
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• 1996

본 연구에서는 음향방출법을 이용하여 로터리 압축기의 인공 결함을 분류하기 위한 연구를 수행하였다. 이를 위해 프로그램을 개발하였고 선형 분류기, 경험적 Bayesian 분류기, 신경 회로망 분류기를 함께 사용하여 비교하였다. 그 결과 신경 회로망 분류기가 인식률 면에서 유리하였으며 신경 회로망 분류기의 경우 99%이상의 인식률을 얻을 수 있었다.

• 한국복합재료학회:학술대회논문집
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• 한국복합재료학회 2002년도 춘계학술발표대회 논문집
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• pp.39-43
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• 2002

As the concept of the smart structure, monitoring of acoustic emission (AE) can be applied to inspect the fracture of the entire structure in operating condition using built-in sensors. The objective of this study is to find the characteristics of matrix crack signals in composites due to the different specimen shapes. To detect matrix crack signals, we performed tensile tests by changing the thickness, width and length of the specimen. For the quantitative evaluation, time frequency analysis such as short-time Fourier transform (STFT) was used to characterize the matrix crack signals from PZT sensor. The experimental result shows the distinctive signal features in frequency domain due to the different specimen shapes.

• 한국정밀공학회지
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• 제21권8호
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• pp.57-64
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• 2004

This study deals with the prediction of defect location which can be occurred in structure. The existing methods was very difficult to be applied to predict it, because of complex numerical formula. The triangulation method proposed in this study can predict the source location easily with small amount of data. The arrival time of wave can be directly converted into the distance between sensors. For this purpose, the propagation velocity was measured by Rayleigh wave, and the propagation behavior was analyzed. The welded workpiece is adapted to investigate for the consideration of jointed part in structure, The propagation velocity of signal was measured in welded workpiece and the revised algorithm of source location was proposed.

• Macromolecular Research
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• 제9권1호
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• pp.20-29
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• 2001

Interfacial adhesion and nondestructive behavior of electrodeposited (ED) carbon fiber rein-forced composites were evaluated using electro-micromechanical techniques and acoustic emission (AE). The interfacial shear strength (IFSS) of the ED carbon fiber/epoxy composites was higher than that of the untreated fiber. This might be expected because of the possibility of chemical or hydrogen bonding in an electrically adsorbed polymeric interlayer. The logarithmic electrical resistivity of the untreated single-carbon fiber composite increased suddenly to infinity when fiber fracture occurred, whereas that of the ED composite increased relatively gradually to infinity. This behavior may arise from the retarded fracture time due to enhanced IFSS. In single- and ten-carbon fiber composites, the number of AE signals coming from interlayer failure of the ED carbon fiber composite was much larger than that of the untreated composite. As the number of the each first fiber fractures increased in the ten-carbon fiber composite, the electrical resistivity increased stepwise, and the slope of the logarithmic electrical resistance increased.

• 비파괴검사학회지
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• 제28권3호
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• pp.254-262
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• 2008

The objective of the proposed study is to produce a tool-condition monitoring (TCM) strategy that will lead to a more efficient and economical drilling tool usage. Drill-wear monitoring is an important attribute in the automatic cutting processes as it can help preventing damages of the tools and workpieces and optimizing the tool usage. This study presents the architectures of a multi-layer feed-forward neural network with back-propagation training algorithm for the monitoring of drill wear. The input features to the neural networks were extracted from the AE signals using the wavelet transform analysis. Training and testing were performed under a moderate range of cutting conditions in the dry drilling of steel plates. The results indicated that the extracted input features from AE signals to the supervised neural networks were effective for drill wear monitoring and the output of the neural networks could be utilized for the tool life management planning.