• 한국가스학회지
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• 제5권1호
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• pp.1-6
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• 2001

고강도 알루미늄 합금 2024-T3와 모제 시험편과 부식 시험편을 사용하여 피로 균열 진전증에 발생하는 음향방출 신호를 검출 분석하였다. 시험편 조건에 관계없이 5종류의 신호가 관찰되었다. 모재에서 얻어진 신호는 피로하중을 받으므로서 균열 발생 초기 노치선단외 돌출과 함몰에 의한 높은 에너지를 가진 짧은 기간의 입내 파괴 신호이며, 부식 시험편은 피트 선단 근처의 결정경계의 치화로 인하여 입계 파괴 신호이다. 따라서, 파형과 파워 스펙트럼은 시험편 조건에 의존하는 것 같다. 시험편 조건에 의한 파형과 파워 스펙트럼을 해석함으로써 구조부재의 다향한 손상과 결함으로부터 발생하는 균열 전파와 최종 파괴과정의 실시간 감시 가능하다

• 한국생산제조학회지
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• 제5권2호
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• pp.38-45
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• 1996

This study was focused on the prediction of the approprite tool life by clarifying the correlation between progressive drill wear and AE signal. on drilling SM45C the following results have been obtained; RMSAE, AE CUM-CNTS had a tendency to increase slowly according to wear size, at 1000rpm, 150mm/min However, these increased suddenly in the range of 0.20~0.22mm wear, about 102 holes and had a tendency to go up and down until the drilling was impossible. The sudden increase of AE signals shows that something is wrong and it is closely connected with drill wear and chipping. It also makes the working surface bad From the above results, AE signals could be used to monitor the drill's condition and to determine the right time to change tools.

• 한국생산제조학회지
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• 제6권2호
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• pp.112-118
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• 1997

Characteristics of AE(Acoustic Emission) signal is related to cutting conditions, tool materials, and tool geometry in metal cutting. Relation between AE signal and tool wear was investigated experimentally. Experiment is carried out by interrupted cutting for SCM420 workpiece with TiN coating tool on HSS material. AE RMS voltage and count per event were increased according to tool wear. The major results are as follows : 1) AE RMS value is nearly constant as cutting speed changes, but is rapidly increase as feed rate increases. 2) AE RMS value and Count per Event increase as tool wear increases. 3) It is more effective to monitor tool wear by Incremental rate of AE RMS value than by Incremental rate of count per event.

• 한국기계가공학회지
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• 제8권1호
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• pp.31-36
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• 2009

Micro-machining technology has been adopted for shape accuracy of micrometer and sub-micrometer scale, surface roughness of tens nanometer in industries. In micro-machining process the quality of machined surface is derived from machining condition and tooling. This paper investigates AE(acoustic emission) and cutting force signals according to machined surface quality related to machining condition. Machined surface quality was analyzed by the AE and cutting force parameter which reflect surface morphology. The characteristics of signal were extracted for process optimization by monitoring both the tool condition and the machined surface texture in micro end milling process.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2011년도 춘계학술대회 논문집
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• pp.582-587
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• 2011

Acoustic Emission(AE) is widely used for early detection of faults for rotating machinery in these days because of its high sensitivity. AE signal has to need for transferring to low frequency range for the spectrum analysis included the fault mechanism. In transferring process, we lose a lot of fault information caused by unusable signal processing method. Discrete Wavelet Transform(DWT) is a method of signal processing for AE signatures, but the pattern of its mother function is not optimized with AE signals. So, we can lose the fault information when we want to use the DWT for AE signal. Therefore, in this paper, we will propose a novel pattern for DWT mother function, which is optimized with AE signals. And it will be applied to compare the results of DWT by daubechie and novel pattern.

• 비파괴검사학회지
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• 제16권2호
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• pp.79-85
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• 1996

복합 재료의 single-lap 및 double-strap adhesive bonding joint 시험편에서 피로 및 인장시험시 방출되는 AE 신호의 분석에 의한 피로 수명 예측 및 파괴 과정의 감시에 대한 연구를 수행하였다. Bonding 시험편의 피로시험시 AE event의 발생양상은 피로에 의해 축적된 elastic strain energy의 방출과 관계되며, peak amplitude가 높고 event duration이 긴 event의 발생 영역과 peak amplitude가 낮고 event duration이 짧으나 많은 event가 발생되는 영역으로 나눌 수 있었다. 그리고 두 영역 사이의 경계 cycle은 피로 수명의 70-80% 근방에 존재하였으며, 이를 이용한다면 피로 수명의 예측이 가능하리라 판단된다.

• 비파괴검사학회지
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• 제36권3호
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• pp.181-187
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• 2016

화력발전용 보일러 내부에는 연소열을 고온 고압의 스팀으로 변환하기 위한 열교환 튜브가 복잡한 형태로 배치되어 있다. 이 튜브에서 누설이 발생할 경우 고압의 스팀에 의해 치명적인 고장과 발전 정지를 초래하며, 누설 발생 여부를 조기에 검출하지 못할 경우 인근 튜브의 손상까지 연쇄적으로 발생하여 조기에 튜브 누설을 검출할 수 있는 기술 확립이 필요하다. 본 논문에서는 기존 보일러 튜브 누설 감시 설비(BTLD)에 음향방출(AE) 기술을 적용하여 기존 시스템 대비 미세누설을 검출하고 조기에 경보를 발생시킬 수 있는 시스템에 대해 개발을 하였다. 또한 이 시스템을 검증하기 위해 실제 운전 중인 560 MW급 화력발전소 보일러에서 다양한 크기(ⵁ2, ⵁ5, ⵁ10 mm)의 모의 누설시험을 실시하였으며, 그 결과 기존 시스템에서는 검출하지 못하였던 미세누설(ⵁ2 mm, ⵁ5 mm)에 대해 개발된 시스템은 조기에 경보를 발생(18 dB 이상 신호 상승)시킬 수 있음을 확인하였다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2008년도 춘계학술대회논문집
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• pp.425-430
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• 2008

To verify the system performance of portable AE leak diagnosis system which can measure with moving conditions, AE activities such as RMS voltage level, AE signal trend, leak rate degree according to AE database, FFT spectrum were measured during operation on total 11 valves of the secondary system in nuclear power plant. AE activities were recorded and analyzed from various operating conditions including different temperature, type of valve, pressure difference, valve size and fluid. The results of this field study are utilized to select the type of sensors, the frequency band for filtering and thereby to improve the signal-to-noise ratio for diagnosis for diagnosis or monitoring of valves in operation. As the final result of application study above, portable type leak diagnosis system by AE was developed. The outcome of the study can be definitely applied as a means of the diagnosis or monitoring system for energy saving and prevention of accident for power plant valve. The purpose of this study is to verify availability of the acoustic emission in-situ monitoring method to the internal leak and operating conditions of the major valves at nuclear power plants. In this study, acoustic emission tests are performed when the pressurized temperature water and steam flowed through glove valve(main steam dump valve) and check valve(main steam outlet pump check valve) on the normal size of 12 and 18 ". The valve internal leak monitoring system for practical field was designed. The acoustic emission method was applied to the valves at the site, and the background noise was measured for the abnormal plant condition. To improve the reliability, a judgment of leak on the system was used various factors which are AE parameters, trend analysis, frequency analysis, voltage analysis and amplitude analysis of acoustic signal emitted from the valve operating condition internal leak.

• 대한기계학회논문집A
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• 제23권6호
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• pp.1011-1017
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• 1999

Electrolytic in-process dressing grinding technique which enables application of metal bond wheels with fine superabrasives in mirror surface grinding operations has developed. It is possible to make efficient precision machining of hard and brittle material such as ceramic and hard metal by the employment of this technique. However, in order to ensure the success of performances such as efficient machining, surface finish, and surface quality, it is important to sustain the insulating layer that has sharply exposed abrasives in wheel surface. Using AE(Acoustic Emission) sensor, this paper will show whether the insulating layer sustains stably or not in real grinding time. And by comparing AErms value and surface roughness their thresholds for stable electrolytic in-process dressing grinding will be determined.

• 한국정밀공학회지
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• 제23권1호
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• pp.64-71
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• 2006

Ultraprecision machining and MEMS technology have been taken more and more important position in machining of microparts. Micro endmilling is one of the prominent technology that has wide spectrum of application field ranging from macro parts to micro products. Also, the method of micro-grooving using micro endmill is used widely owing to many merit, but has problems of precision and quality of products due to tool wear and tool fracture. This investigation deals with state monitoring using acoustic emission(AE) signal in the micro-grooving. Characteristic evaluation of AE raw signal, AE hit and frequency analysis for condition monitoring is presented. Also, 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 the fuzzy C-means algorithm.