• 한국기계가공학회지
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• 제9권4호
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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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 C
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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.

• 비파괴검사학회지
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• 제21권1호
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• pp.54-61
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• 2001

웨이블릿 변환과 인공신경망을 이용하여 AE 신호를 분류하는 소프트웨어 패키지를 개발하였다. 웨이블릿 변환으로는 연속 웨이블릿 변환과 이산 웨이블릿 변환을 모두 고려하였으며, 인공신경망의 모델로는 오류 역전파 인공신경망을 사용하였다. 분류에 사용된 AE 신호는 용접부에 인공결함을 가진 시편의 3점 굽힘시험에서 발생한 신호이다. 개발된 소프트웨어 패키지를 이용하여 이 신호를 웨이블릿 변환시켜 생성된 시간-주파수 평면상에서 특징값을 추출하고 이를 인공신경망에 학습하여 인공신경망 분류기를 설계하고 검증하였다. 본 연구에서 개발된 소프트웨어 패키지를 이용한 AE 신호 분류법이 유용함을 보이고, 또한 연속 웨이블릿 변환과 이산 웨이블릿 변환에 의한 분류 결과를 비교하였다.

• 비파괴검사학회지
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• 제23권5호
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• pp.429-438
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• 2003

본 연구에서는 연속형 일방향 및 수자직 유리섬유강화 복합재료로 인장 시험편을 만들어 AE 특성에 대한 섬유배향 효과를 살펴보았다. AE 신호들을 STFT 처리하여 특성별로 분류하였으며, 반사식 및 투과식 편광현미경을 이용하여 시험편의 손상영역을 관찰하였다. 저주파수 대역의 약한 AE 신호들은 모재 및 계면에서의 손상으로 나타났으며, 높은 진폭의 고주파수 대역 AE 신호들은 섬유 파단에 기인하였다. 섬유 파단 과정에서 발생하는 고진폭의 AE 사상률을 기본 특성 데이터로 하여 다른 섬유배향과 노치방향을 가진 복합재에서의 파괴과정을 특징지을 수 있었다. 결론적으로, AE범은 연속형 유리섬유 강화 복합재의 파괴거동을 탐지하는데 있어 유용함을 알 수 있었다.

• 조명전기설비학회논문지
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• 제20권2호
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• pp.75-80
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• 2006

본 논문은 전력 케이블의 절연체인 XLPE 시료에 상용주파수 교류전압을 인가하였을 때, 절연열화에 의한 보이드방전 펄스신호와 AE신호 및 트리진전 특성을 검출하고 관찰하였다. 또한 XLPE 절연체의 트리 채널내 인가전압에 따라 보이드방전 펄스신호와 AE신호를 각각 관측하였다. 실험 결과 전력 케이블의 절연체인 XLPE 시료의 보이드 존재 유 무에 따른 트리진전 형상은 보이드 존재시에는 수지형으로 성장하였으나, 무보이드시에는 수초형으로 성장하였다. XLPE 절연체내의 트리진전 특성은 보이드 존재시에는 열화시간이 경과함에 따라 급격히 증가하였으나, 무보이드시에는 열화 시간이 경과함에 따라 트리의 성장 형상은 감소되는 것으로 나타났다. 전력 케이블 절연체인 XLPE의 트리 채널내 보이드방전에 의한 AE신호의 주파수 스펙트럼으로 분석한 결과, AE신호의 크기에 따라 변화되었으며 그 주파수 영역은 대략 1.0[MHz] 이하의 분포인 것으로 관측되었다.

• 대한기계학회논문집A
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• 제25권8호
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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.

• 대한기계학회논문집A
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• 제33권5호
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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.

• 비파괴검사학회지
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• 제23권5호
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• pp.480-487
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• 2003

식물의 환경제어 향상을 위하여, 비파괴적인 방법을 사용하여 식물의 신호 특성이 연구되고 있다. 본 연구에서는 속씨식물과 겉씨식물에서 검출되는 음향방출 신호에 미치는 물 스트레스의 영향에 대하여 검토하였다. 낮 시간의 음향방출 신호는 밤 시간 보다 훨씬 많았으며, 낮 시간의 신호는 식물의 물 스트레스에 의해서 발생한 것으로 판단된다. 속씨식물과 겉씨식물은 음향 신호의 강한 주파수 대역이 다르게 나타났으며, 실외에서 검출한 식물의 음향 신호 주파수는 동일한 식물을 사용하여 실내에서 검출한 신호의 주파수 범위와 일치하였다.

• 한국생산제조학회지
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• 제7권6호
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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.

• 한국해양공학회지
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• 제17권3호
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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.