• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.97-101
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• 2000

The AE measurement is one of the most convenient methods for detecting contacts between slider disk. The AE method has been widely used in the investigation of the tribology of sliding interfaces due to the convenience of using AE sensor. In this study, we examined the relationship between the AE signal and the flying height of a slider. We tried to know the influence of the disk velocity on the AE rms signal by using the AE measurement system. The experiment also gives the relationship between the take-off velocity and the disk surface state. To investigate the behavior of the slider further, the variances of the AE signal are analyzed. The results about a subambient pressure slider indicate that the increase in the magnitude of AE rms signal does not necessarily mean the slider/disk contacts.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.9
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• pp.1391-1399
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• 2001

The AE measurement is one of the most convenient methods for detecting contacts between the slider and the disk. The AE method has been widely used in the investigation of the tribology of sliding interfaces due to its convenience. We examined the relationship between the AE signal and the flying height of a slider. We investigated the influence of the disk linear velocity on the AE rms signal by using the AE measurement system. The experiment also gives the relationship between the take-off velocity and the disk surface conditions. To investigate the behavior of the slider further, the variances of the AE signals are analyzed. The experimental results indicate that the increase in the magnitude of the AE rms signal does not necessarily mean the slider/disk contacts.

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.2
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• pp.155-169
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• 2002

The objective of this study was to investigate the effect of crack opening and closure in the AE activities during fatigue test. Laboratory experiment using various materials and test conditions were carried out to identify AE characteristics of fatigue crack propagation. Compact tension specimens of 2024-T4 and 6061-T6 aluminum alloy were prepared for fatigue test. AE activities were analyzed based on the phase of the loading cycle. Generally, most of AE were generated when the crack begins' opening and the crack closes fully, whereas a few in the pull opening of the crack. Also AE activity in the peak loading of cycle was different with each specimen. However, in the same material, AE activity was not affected by the change of cyclic frequency (0.1, 0.2, 1.0Hz). It was found that AE activities during crack opening and closure depend on material properties such as micro-structure, tensile strength and yield strength.

• Journal of Biosystems Engineering
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• v.29 no.3
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• pp.261-266
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• 2004

This study was Performed to classify the acoustic emission(AE) signal due to surface cracking and moisture movement in the flat-sawn boards of oak(Quercus Variablilis) during drying using the principal component analysis(PCA) and artificial neural network(ANN). To reduce the multicollinearity among AE parameters such as peak amplitude, ring-down count event duration, ring-down count divided by event duration, energy, rise time, and peak amplitude divided by rise time and to extract the significant AE parameters, correlation analysis was performed. Over 96 of the variance of AE parameters could be accounted for by the first and second principal components. An ANN analysis was successfully used to classify the Af signals into two patterns. The ANN classifier based on PCA appeared to be a promising tool to classify the AE signals from wood drying.

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.5
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• pp.532-538
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• 2002

This study was performed to classify the fatigue crack opening and closure for three kinds of aluminum alloy using principal component analysis (PCA). Fatigue cycle loading test was conducted to acquire AE signals which come from different source mechanisms such as crack opening and closure, rubbing, fretting etc. To extract the significant feature from AE signal, correlation analysis was performed. Over 94% of the variance of AE parameters could accounted for the first two principal components. The results of the PCA on AE parameters showed that the first principal component was associated with the size of AE signals and the second principal component was associated with the shape of AE signals. An artificial neural network (ANN) an analysis was successfully used to classify AE signals into six classes. The ANN classifier based on PCA appeared to be a promising tool to classify AE signals for fatigue crack opening and closure.

• Composites Research
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• v.27 no.4
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• pp.152-157
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• 2014

Non-destructive testing methods of composite materials are very important for improving material reliability and safety. AE measurement is based on the detection of microscopic surface movements from stress waves in a material during the fracture process. The examination of AE is a useful tool for the sensitive detection and location of active damage in polymer and composite materials. FBG (Fiber Bragg Grating) sensors have attracted much interest owing to the important advantages of optical fiber sensing. Compared to conventional electronic sensors, fiber-optical sensors are known for their high resolution and high accuracy. Furthermore, they offer important advantages such as immunity to electromagnetic interference, and electrically passive operation. In this paper, the crack detection capability of AE (Acoustic Emission) measurement was compared with that of an FBG sensor under tensile testing and buckling test of composite materials. The AE signals of the PVDF sensor were measured and an AE signal analyzer, which had a low pass filter and a resonance filter, was designed and fabricated. Also, the wavelength variation of the FBG sensor was measured and its strain was calculated. Calculated strains were compared with those determined by finite element analysis.

• Journal of Korean Tunnelling and Underground Space Association
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• v.3 no.1
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• pp.51-62
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• 2001

In-situ stress measurement using AE (Acoustic Emission) and DRA (Deformation Rate Analysis) is usually carried out under uniaxial loading in the laboratory and it consumes delay time from drilling to testing. Therefore, it should be considered how the lateral stress and delay time influence on the test results for the in-situ stress determination. As the delay time increased, the accuracy of estimating the pre-stress decreased. The pre-stress of the specimen loaded only axially was determined within an error of less than 9% (using AE) and 4% (using DRA). And the specimen on which axial pre-stress and the confining pressure were loaded had an error of less than 17% (using AE) and 14% (using DRA). The results of AE and DRA for field specimens were very similar with each other but smaller than those of hydraulic fracturing method.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.146-149
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• 2003

This paper proposes the concept of “Artificial Emotion”(AE). The goal of AE is simulation, extension and expansion of natural emotion, especially human emotion. The object of AE is machine emotion and emotion machine. The contents of AE are emotion recognition, emotion measurement, emotion understanding emotion representation emotion generation, emotion processing, emotion control and emotion communication. The methodology, technology, scientific significance and application value of artificial emotion are discussed

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.560-564
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• 1999

Burr sensing for burr size measurement and deburring process control is one of the essential elements in an automated deburring procedure. This paper presents the implementation of capacitance sensing and acoustic emission (AE) to deburring. The first application is the "on-line" measurement of burrs using a capacitance sensor. A non-contact capacitance gauging sensor is attached to an ultra precision milling machine which was used as a positioning system. The setup is used to measure burr profiles along machined workpiece edges. The proposed scheme is shown to be accurate, easy to setup, and with minor modifications, readily applicable to automatic deburring processes. As the second example, AE signals were sampled and analyzed for the sensor feedback of a precision deburring process - laser deburring -. The results, such as the sensitivity of AE signals to burr shapes and edge detection capability show a clear correlation between physical process parameters and the AE signals. A subsequent control strategy for deburring automation is also briefly discussed.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.4
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• pp.69-76
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• 1994

AE(Acoustic Emission) signal is correlated to workpiece material, cutting conditions and tool geometry during metal cutting. The relationship between AE signal and cutting parameters can be obtained by theoretical model and experiments. The value of CR(Count Rate) is nearly constant in stable cutting, but when the chatter vibration occours, the value of CR is rapidly increased due to the vibration deformation zone. By experimental signal processing of AE, it is more effective than by RMS(Root Mean Square) measurement to detect the threshold of chatter vibration by CR measurement.