• Journal of the Korean Institute of Gas
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• v.5 no.1
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• pp.1-6
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• 2001

The main objective of this study is to determine if the sources of AE in corroded specimens of aluminum could be identified iron the characteristics of the waveform signals recorded during fatigue loading. Coupons of notched 2024-T3 aluminum with or without corrosion (at the notch) were subjected to fatigue loading and the AE signals were recorded using non-resonant, flat, wide-band transducers. The time history and power spectrum of each individual wave signal recorded during fatigue crack growth were examined and classified according to their special characteristics. Five distinct types of signals were observed regardless of specimen condition. The waveform and power spectra were shown to be dependent on specimen condition. During the initial phase of crack growth, the signals obtained in the as-received specimens are most probably due to transgranular cleavage caused by extrusion and intrusion under fatigue loading. In the corroded specimen the signal are probably generated by intergranular cleavage due to embrittlement of grain boundary neat the pitting tip. The need for additional research to further validate these findings is indicated.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.5 no.2
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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.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.2
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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.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.8 no.1
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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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.04a
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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.

• Journal of the Korean Society for Nondestructive Testing
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• v.16 no.2
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• pp.79-85
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• 1996

Prediction of fatigue life and monitoring of fracture process for adhesively bonded CFRP composites joint have been investigated by analysis of acoustic emission signals during the fatigue and tension tests. During fatigue test, generated acoustic emission is related to stored elastic strain energy. By results of monitoring of AE event rate, fatigue process could be divided into two regions, and boundaries of two regions, fatigue cycles of the initiation of fast crack growth, were 70-80% of fatigue life even though the fatigue life were highly scattered from specimen to specimen. The result shows the possibility of predicting catastrophic failure by acoustic emission monitoring.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.3
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• pp.181-187
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• 2016

A thermal power plant has a heat exchanger tube to collect and convert the heat generated from the high temperature and pressure steam to energy, but the tubes are arranged in a complex manner. In the event that a leakage occurs in any of these tubes, the high-pressure steam leaks out and may cause the neighboring tubes to rupture. This leakage can finally stop power generation, and hence there is a dire need to establish a suitable technology capable of detecting tube leaks at an early stage even before it occurs. As shown in this paper, by applying acoustic emission (AE) technology in existing boiler tube leak detection equipment (BTLD), we developed a system that detects these leakages early enough and generates an alarm at an early stage to necessitate action; the developed system works better that the existing system used to detect fine leakages. We verified the usability of the system in a 560MW-class thermal power plant boiler by conducting leak tests by simulating leakages from a variety of hole sizes (ⵁ2, ⵁ5, ⵁ10 mm). Results show that while the existing fine leakage detection system does not detect fine leakages of ⵁ2 mm and ⵁ5 mm, the newly developed system could detect leakages early enough and generate an alarm at an early stage, and it is possible to increase the signal to more than 18 dB.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.23 no.6 s.165
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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.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.1 s.178
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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.