• 한국기계가공학회지
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• 제11권2호
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• pp.34-39
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• 2012

In this study, the friction welding experiment was performed by using the design of experiment. And the signal data acquired by acoustic emission sensor were analyzed to predict the tensile strength of friction welding part at friction welding process for AZ31 magnesium alloy. A dimensionless coefficient($\phi_{AE}$), which consisted in the square of AE rms and variance, was defined as the characteristic of friction welding and the prediction equation was obtained by using linear regression. As the result of analysis, it was seen that the correlation between predicted and measured values became very close and on-line prediction of the ensile strength was possible in friction welding part.

• 비파괴검사학회지
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• 제24권5호
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• pp.465-475
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• 2004

본 논문은 두 편으로 구성된 스마트 능동 레이어(smart active layer, SAL) 센서 개발에 관한 첫 번째 논문으로, 구조 내에 발생하는 균열 및 손상에 의한 탄성파 검출을 위한 SAL 센서의 이론 및 개념을 연구하였다. 본 SAL 센서의 개발을 위해, 첫째, 탄성파의 기본 이론을 고찰하였고, 둘째, 이론적 토대 위에 단일 압전 disc의 유한요소해석법(finite element analysis, FEA)을 이용하여 탄성파 검출 센서로서의 가능성을 검증하였고, 셋째, 몇 종류의 압전 disc 센서와 상용 음향방출(acoustic emission, AE) 센서를 연필심 파괴시험을 통해 상호 성능을 비교하여 실험적으로 그 가능성을 확인하였다. 또한 수 개의 센서를 일정한 거리로 분포시켜서 구조물 결함의 효과적 검출 및 위치 표정이 가능한 스마트능동레이어 센서의 개념연구를 수행하였다.

• 한국기계가공학회지
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• 제9권3호
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• pp.76-82
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• 2010

A laborer always watched a process of production carefully but defective manufactures were inspected after press process. These inspections made a waste of human power and defective manufactures could make a serious damage of press mold. Therefore, AE(Acoustic Emission) system was introduced to prevention of the damage of the press molds, to a real time detection of defective manufactures and to save human power. AE system was introduced to solve this problem which is a detecting defective manufacture on real time and to prevent the damage of the press mold. In this research we get acoustic emission signal in accordance with weight and processing method of press by using AE sensor, Preamplifier, AE board signal board which occurs press processing and it analyzed various signal through using CMD8 software on the time. From the result, we found that the intensity and shape of the signal were changed according to the weight and processing type of the press. By using this special algorithm, it can judge the acoustic signal which occurs from press on real time.

• 비파괴검사학회지
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• 제29권5호
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• pp.466-472
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• 2009

고온, 고압의 원자력 배관 누설 판별을 위해 음향방출기법(AE)을 이용한 누설감지 시스템인 ALMS 기법이 적용되고 있다. 이 시스템은 단지 AE 센서로 전해진 신호의 RMS값을 이용하여 누설의 유무만을 판단할 뿐, 누설 발생시 누설부의 크기나 형태를 평가하는 것에는 어려움이 있었다. 따라서 본 논문에서는 이러한 문제를 해결하기 위하여 AE센서와 가속도센서를 동시에 이용한 이중 센서 시스템을 제안하였다. 빠른 학습 속도와 정확성을 위해 Levenberg-Marquardt 학습 알고리즘을 이용한 인공신경회로망을 적용시키고, 이를 통해 신뢰성 있는 분석 결과를 얻을 수 있다. 배관내 압력과 누설부의 크기와 모양에 따른 실험신호들을 학습시키고 그 판별 정확성을 확인하였다. 추가적으로 배관 두께에 따라 발생하는 파(wave)의 종류와 특성이 달라지는 것을 이론과 실험을 통하여 알아보았다.

• 한국정밀공학회지
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• 제22권10호
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• pp.90-98
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• 2005

A monitoring system is necessary to make the polishing process more reliable in order to ensure the high quality and performance of the final products. Generally, AE (Acoustic Emission) is known to be closely related to the material removal rate (MRR). As the surface becomes rougher, the MRR and AE increase. Therefore, the surface roughness can be indirectly estimated using the AE signal measured during the polishing. In this study, an AE sensor-based monitoring system was fabricated to detect the very small AE signal resulting from the friction between a tool and a workpiece during polishing. The performance of this monitoring system was estimated according to polishing conditions, the relation between the level of the AE RMS and the surface roughness during the polishing was investigated.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2000년도 춘계학술대회논문집 - 한국공작기계학회
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• pp.39-45
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• 2000

This paper describes the fundamental investigations on the in-process monitoring techniques focused on Acoustic Emission(AE) based on analytical method. Experiments were conducted on a CNC lathe using conventional carbide insert tools under various cutting conditions. As the result of this study a suggestion is given about the multi-purpose use of AE-signals detected with a single sensor for the monitoring of tool wear, built-up edge and chatter vibration in turning process.

• 한국정밀공학회지
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• 제22권6호
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• pp.109-117
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• 2005

In case of fine machining processes, the cutting state monitoring by a skilled operator is impossible because the physical changes generated during fine machining are very weak. To realize the high efficient and precise fine machining, it is necessary to develop the sensor based monitoring system which is able to detect the fine changes of cutting state. In this paper, the fine acoustic emission monitoring system is developed to monitor the state of the fine machining process. The developed system consists of the AE sensor and the AE signal processing unit. And this has the high-sensitivity and bandwidth which can detect fine AE signal generated during fine machining process. In order to investigate the feasibility of the developed system, evaluation experiments were performed in the fine fixed-abrasive machining processes such as polishing and glass ferrule slicing. Experimental results show that the developed monitoring system possesses an excellent real-time monitoring capability at fine machining processes.

• Tribology and Lubricants
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• 제35권5호
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• pp.317-322
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• 2019

In spite of progress in tribological research, machine component failure due to friction and wear has been reported frequently. This failure may lead to secondary damage that can cause huge expense for maintenance and repair. To prevent economic loss, it is important to detect and predict the initial failure point. In this sense, various researchers have been tried to develop Condition Monitoring (CM) method using Acoustic Emission (AE) generated while the materials undergo failure. In this study, effect of particles on friction and wear was investigated using the pin-on-plate friction test and AE signal was recorded with a band-width type AE sensor. The experiments were performed in dry and lubricant conditions using steel and glass as specimens. After the experiment, 3D laser microscope image was captured to evaluate the wear behavior quantitatively. The AE signal was analyzed in time-domain and frequency-domain. The amplitude was compared with the frictional results. The results of this study showed that particle generation accelerate wear, generate high magnitude AE signal and change the frequency characteristics of the signal. Also, lubricant condition test results showed low coefficient of friction, low wear rate, and low magnitude of AE signal compared to the dry condition. It is expected that the results of this study will aid in better assessment of wear in CM technology

• 한국생산제조학회지
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• 제20권3호
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• pp.304-309
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• 2011

A monitoring system for magnetic abrasive polishing process is necessary to ensure the polishing products the high quality and integrity. Acoustic emission (AE) signal is known to reflect the material removal phenomena in other machining processes. In a case of the magnetic abrasive polishing of non-magnetic materials, application of AE method is very difficult because of lower machining force on the surface of workpiece and the level of AE signal is extremely lower. In this study, AE sensor-based monitoring system is applied to the magnetic abrasive polishing. The relation between the level of the AE RMS and the surface roughness during the magnetic abrasive polishing of magnesium alloy steel is investigated.

• 한국전기전자재료학회논문지
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• 제13권6호
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• pp.502-508
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• 2000

In order to detect the partial discharge with the metallic particle in GIS the AE(Acoustic Emission) sensor was designed and simulated by ANSYS 5.5 and manufactured as the coupled vibration mode. The measured resonant frequency and the maximum sensitivity frequency of three coupled AE sensors were as follows ; 147.88 kHz in 8.1mm $\Phi$$\times$8.1mm 128.82 kHz and 58.8 kHz in 9.5 mm$\Phi$$\times$9.5mm, 85.22 kHz and 32.6 kHz in 14.3 mm$\Phi$$\times$14.3 mm, resonant frequency of the AE sensor. The AE sensor of 9.5 mm$\Phi$$\times$9.5mm responded higher than the other coupled vibration mode AE sensor at the partial discharge detection in GIS.