• 동력기계공학회지
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• 제13권4호
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• pp.43-48
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• 2009

The purpose of this study is to show how to eliminate the noises and to obtain the data related with the relationship between AE signal characteristics and mechanical behaviors for the pressure vessel steel. Various kinds of noises are introduced into the AE data in the course of experiments. Accordingly, real AE data have to be obtained after tests. AE test was carried out under four point bending load. Among AE signals, counts and signal strength are used to find out the differences of AE characteristics between the basemetal and weldment. After tests, this paper shows the procedures of filtering the noises against basemetal of the pressure vessel steel to obtain the real data around crack tip. Relationships between plastic zone size and cumulative AE counts are shown also. AE signals were absent within an elastic region, regardless of the specimens. Most of AE signals are produced in the process of plastic deformation. The deformation and fracture modes of basemetal and weldment are quiet different.

• 한국재료학회지
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• 제17권5호
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• pp.239-243
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• 2007

The evaluation and comparison have been made for the EMI noise which was included in the signal from the sensors in the acoustic emission testing for the bottom plate of ground tank at full. The EMI signal has been classified into two types. One is the signal with very short AE count, and this signal possibly can be filtered by front end filter setting of the channel count with low level of 4 and high level of $10^8$. The other EMI signal occurred from CH 1, CH 3 and CH 10, and had high and constant duration with high energy and count (maximun duration > $10^5\;{\mu}s$), and has characteristic gradient of accumulation amplitude distribution. This signal should be removed in the AE signal evaluation by filtering, because this may affect to the total gradient.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2004년도 학술대회지
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• pp.360-364
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• 2004

The objective of this study is to estimate the feasibility of acoustic emission method Jar the internal leak from the valves in nuclear power plants. The acoustic emission method was applied to the valves at the site, and the background noise was measured for the abnormal plant condition. From the comparison of background noise data with the experimental results as to relation between leak flow and acoustic signal, the minimum leak flow rates that am be detected by acoustic signal was suggested. When the background noise level are higher than the acoustic signal, the method described below was considered that the analysis the remainder among the background noise frequency spectrum and the acoustic signal spectrum.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회A
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• pp.938-942
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• 2008

To detect the failures in machine, the signals of current, vibration and acoustic emission are widely used in industry. And unexpected failures of gears are not only extremely damaging but also lead to economic losses. In this paper, to detect the misalignment occurred at between two gears in gearboxes, the signals of current, vibration and AE were measured at gearbox and motor power line. FFT(Fast Fourier Transform) was used for current and vibration signal analysis to find gear failure frequency. Especially, the envelop analysis and wavelet transform were applied for AE signal. Therefore, compared with the results of three kinds of signal, the possibility of earily detection by AE is identified or checked.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.101-104
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• 1995

A acoustic emission (AE) sensor has been used to monitor tool were during milling process. The relation between tool wear and AE RMS (Root mean Square) signal was investigated experimentally. A avaliable monitoring index for monitoring toolwear was newly extracted form AE RMS. And on-line monitoring program was developed. The proposed monitoring system has verified experimentally by roughing end milling titanium alloy with TIN coated HSS tool.

• 한국공작기계학회논문집
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• 제18권2호
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• pp.170-177
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• 2009

The objective of this study is to evaluate an acoustic emission (AE) source characterization and fracture behavior of the SM45C steel by using back-propagation neural network (BPN). In previous research Ref. [8] about k-nearest neighbor classifier (k-NNC) continuity, we used K-means clustering method as an unsupervised learning method for obtaining multi-variate AE main data sets, such as AE counts, energy, amplitude, risetime, duration and counts to peak. Similarly, we applied k-NNC and BPN as a supervised learning method for obtaining multi-variate AE working data sets. According to the error of convergence for determinant criterion Wilk's ${\lambda}$, heuristic criteria D&B(Rij) and Tou values are discussed. As a result, in k-NNC before fracture signal is detected or when fracture signal is detected, showed that produce some empty classes in BPN. And we confirmed that could save trouble in AE signal processing if suitable error of convergence or acceptable encoding error give to BPN.

• 정보저장시스템학회논문집
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• 제3권4호
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• pp.160-166
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• 2007

In this paper, the measured electrical resistance method is proposed to analyze the ramp-tab contact during the load/unload (L/UL) process. Since this method supplies the voltage change due to the resistance change, we can easily and conveniently identify the ramp-tab contact from the acoustic emission (AE) signal. At first, we carefully deposit the conductive material on the surface of the conventional ramp by sputtering method. The ratio frequency (RF) magnetron co-sputtering system is applied to accomplish the deposited double-layers on the ramp surface. One layer is the stainless steel for the conductive layer and the other is the titanium layer for the cohesive function between the ramp surface and the stainless steel layer. In order to guarantee the stiffness and damping properties of the original ramp, the deposited conductive layer is intended to have very thin thickness. After integration the proposed ramp device into the L/UL system and networking the electrical resistance circuit, the L/UL performance is experimentally evaluated by comparing the measured electrical resistance signal and AE signal.

• 오토저널
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• 제8권3호
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• pp.28-36
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• 1986

This study was investigated the feasibility of AE application on in-process detection of tool fracture and chipping. Carbon steel SM45C workpiece with longitudinal slots was turned interruptedly on a lathe. AE RMS signal at tool fracture was observed and also the tangential force and the feed observed at the time of tool fracture, the levels of tangential force and the feed force at the time of fracture decrease considerably. In chipping, high level AE signal was observed but there were no changes of cutting force. Peak AE RMS squared is proportional to the area of tool fracture and resultant force. Fracture model of tool fracture is proposed as $V_{p}$ = $C_{1}$ $E_{1}$F(.DELTA. A)$_{0.5}$ and peak AE RMS shows strong correlation with the fracture parameter F(.DELTA.A)$^{0.5}$.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 2002년도 proceedings of the second asia international conference on tribology
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• pp.191-192
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• 2002

The scuffing failure is a critical problem in modern machine components, especially for the requirement of high efficiency and small size. In this study. scuffing experiments are conducted using Acoustic Emission(AE) measurement by an indirect sensing approach to detect scuffing failure. Using AE signals we con get and indication about the state of the friction processes, about the quality of solid and liquid layers on the contacting surface in real time. The FFT(Fast Fourier Transform)analyses of the AE signal are used to understand the interfacial interaction and the relationship between the AE signal and the state of contact is presented.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1997년도 추계학술대회 논문집
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• pp.59-64
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• 1997

This paper investigates the relationship between cutting conditions and Acoustic Emission(AE) signals; AEavg, AErms, AEmode, as the base working to monitor the tool wear with in-process. For this purpose, cutting tests were conducted on a CNC lathe with comprehensive cutting conditions.. It is known that AEavg and AErms are proportionaly increased as the increasing of cutting velocity and depth of cut respectively. It is also known that AEmode among three kinds of AE signals may be applied for in-process monitoring to make the self diagnosis system because of its stability to the variation of cutting condition.