• 소성∙가공
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• 제18권2호
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• pp.144-149
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• 2009

Recently, the monitoring technology of machining process is very important to improve productivity and quality in manufacturing filed. Such monitoring technology has been performed to measurement using vibration signal, acoustic emission signal and tool dynamometer. However, micro machining is limited small-scale parts machining because micro tool is very small and weakness to generate signal in micro machining process. Therefore, this study has efficient sensing technology for real monitoring system in micro machine that is proposed to supplement a disadvantage of single-sensor by multi sensor. From experimental result, it was evaluated tool wear and cutting situation according to repetitive slot cutting condition and changing cutting condition, and it was performed monitoring spindle rpm and condition according to compare acceleration signal with current signal.

• 한국철도학회논문집
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• 제4권3호
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• pp.79-86
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• 2001

The signal pattern recognition method by acoustic emission signal is applied to detect and classify the defects of a journal bearing in a power plant. AE signals of main defects such as overheating, wear and corrosion are obtained from a small scale model. To detect and classify the defects, AE signal pattern recognition program is developed. As the classification methods, the wavelet transformation analysis, the frequency domain analysis and time domain analysis are used. Among three analyses, the wavelet transformation analysis is most effective to detect and classify the defects of the journal bearing..

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집A
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• pp.65-71
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• 2001

In this paper, acoustic omission technique(AE) has been applied to detect leak for heat exchanger by analyzing the characteristics of signal obtained from leak. It was confirmed that the characteristics of the signal generated by the turbulence of gas in the heat exchanger is narrow band signal having between 130-250KHz. Generally, the amplitude of leak signal is increased as the leak size increasing, but showed no significant change at frequency characteristic. Leak source location can be found by searching for the point of highest signal amplitude by comparing wi th several fired sensors.

• 한국정밀공학회지
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• 제21권9호
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• pp.63-68
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• 2004

The high pressure vessels that are constructed by welding process have many welding lines and most of the leakage defects are occurred on these welding lines. The acoustic emission(AE) technique has adopted to detect the defect location and leakage on welding parts, but the AE signal in leakage are incomplete due to the attenuation, reiteration, instability and limit of defect size. To overcome these troubles, the experiments in this study are conducted to measure the indirect media-propagated AE signal perpendicular to the leakage hole. The AE signals that are acquired from the direct and indirect media are analyzed, and the reliability of the indirect media-propagated AE signal are examined experimentally. By AE signal investigation, this method can be adopted to detect efficiently the leakage in welding parts.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 2002년도 제35회 춘계학술대회
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• pp.34-39
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• 2002

The surfaces of machine components in sliding contact such as bearing, gears and pistons etc. frequently operate under the condition of mixed lubrication due to high load, high speed and slip. These machine components often undergo the inception of scuffing in practical application. The scuffing failure is a critical problem in modern machine components, especially for the requirement of high efficiency and small size. However, it is difficult to find a universal mechanism to explain all scuffing phenomena because there are so many factors affecting the onset of scuffing. In this study, scuffing experiments are conducted using Acoustic Emission(AE) measurement by an indirect sensing approach to detect scuffing failure. Acoustic Emission(AE) signal has been widely utilized to monitor the interaction at the friction interface. Using AE signals we can get an indication about the state of the friction processes, about the quality of solid and liquid layers eon the contacting surfaces 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

• Computers and Concrete
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• 제20권5호
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• pp.595-603
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• 2017

Piezoceramic transducers have been widely used in the health monitoring of civil structures. However, in most cases, they are used as sensors either to measure strain or receive stress waves. This paper proposes a method of using piezoelectric transducers as strain gauges and acoustic emission (AE) sensors simultaneously. The signals received by piezoceramic transducers are decomposed into different frequency components for various analysis purposes. The low-frequency signals are used to measure strain, whereas the high-frequency signals are used as acoustic emission signal associated with local damage. The b-value theory is used to process the AE signal in piezoceramic transducers. The proposed method was applied in the bending failure experiments of two reinforced concrete beams to verify its feasibility. The results showed that the extracted low-frequency signals from the piezoceramic transducers had good agreement with that from the strain gauge, and the processed high-frequency signal from piezoceramic transducers as AE could indicate the local damage to concrete. The experimental results verified the feasibly of structural health monitoring using piezoceramic transducers as strain gauges and AE sensors simultaneously, which can advance their application in civil engineering.

• Tribology and Lubricants
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• 제18권5호
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• pp.351-356
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• 2002

The surfaces of machine components in sliding contact such as bearing, gears and pistons etc. frequently operate under the condition of mixed lubrication due to high load, high speed and slip. These machine components often undergo the inception of scuffing in practical application. The scuffing failure is a critical problem in modern machine components, especially for the requirement of high efficiency and small size. However, it is difficult to find a universal mechanism to explain all scuffing phenomena because there are so many factors affecting the onset of scuffing. In this study, scuffing experiments are conducted using Acoustic Emission(AE) measurement by an indirect sensing approach to detect scuffing failure. Acoustic Emission(AE) signal has been widely utilized to monitor the interaction at the friction interface. Using AE signals we can get an indication about the state of the friction processes, about the quality of solid and liquid layers on the contacting surfaces in real time. The FFT(Fast Fourier Transform) analyses of the AE signal are sued to understand the interfacial interaction and the relationship between the AE signal and the state of contact is presented.

• Journal of Welding and Joining
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• 제30권4호
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• pp.24-30
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• 2012

Laser welding by fiber laser accompanied by a lot of spatter and humping bead. This is because the deep and narrow keyhole usually form due to high beam quality. So the weld bead is formed defects, because the plasma jet with a high vapor pressure make the molten pool on keyhole wall scattered. For such a reason, unstable behavior of keyhole is difficult to monitor laser welding by using the laser induced plasma. Mostly, fiber laser welding of thick plates most be influenced by this effect. Therefore, fiber laser welding has been difficult to apply the sole. Thus, laser welding monitoring based on plasma measurements have much difficulty in measurements and analysis of signal. In this study, influence of the plasma emission signal according to welding speed and laser power in fiber laser welding analysed by using RMS and FFT analysis. We can verify that RMS value of the plasma emission signal changes with welding parameters in fiber laser welding, and aspect ratio greater than 1, the peak of FFT frequency had been moved in accordance with welding parameter.

• 한국생산제조학회지
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• 제6권4호
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• pp.46-53
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• 1997

This paper presents a method of detection localized defects on tapered roller bearing in main spindle of machine tool system. The feature vectors, i.e. statistical parameters, in time-domain analysis technique have been calculated to extract useful features from acoustic emission signals. These feature vectors are used as the input feature of an neural network to classify and detect bearing defects. As a results, the detection of bearing defect conditions could be sucessfully performed by using an neural network with statistical parameters of acoustic emission signals.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2000년도 춘계학술대회논문집A
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• pp.67-72
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• 2000

Demand for now nondestructive evaluation are growing to detect fatigue crack growth behavior to predict long term performance of materials and structure in aggressive environments especially when they are In non-visible area. Acoustic emission technique is well suited to these problems and has drawn a keen interests because of its dynamic detection ability, extreme sensitivity and location of growing defects. In this study, we analysed acoustic emission signals obtained in fatigue and tensile test of high strength fire resistance steel for frame structure with time-frequency analysis methods. The main frequency range is different in the noise and the fatigue crack propagation. It could be classified that it were also generated by composite fracture mechanics of cleavage, dimple, inclusion separation etc.