• Journal of Mechanical Science and Technology
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• v.17 no.11
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• pp.1599-1607
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• 2003

The characteristics of elastic waves emanated from crack initiation in 6061 aluminum alloy subjected to fatigue loading are investigated through experiments. The objective of the study is to determine the differences in the properties of the signals generated from fatigue test and also to examine if the sources of the waves could be identified from the temporal and spectral characteristics of the acoustic emission (AE) waveforms. The signals are recorded using nonresonant, flat, broadband transducers attached to the surface of the alloy specimens. The time dependence and power spectra of the signals recorded during the tests were examined and classified according to their special features. Six distinct types of signals were observed. The waveforms and their power spectra were found to be dependent on the crack propagation stage and the type of fracture associated with the signals. The potential application of the approach in health monitoring of structural components using a network of surface mounted broadband sensors is discussed.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.5 no.3
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• pp.15-21
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• 1996

This study is focused on the prediction of appropriate tool life by clarifying the correlation between progressive tool wear and AE(Acoustic Emission) signals, while cutting stainless steel by end mill on the machining center. The results of this study were that RMSAE tends to increase linearly along with the increase of the cutting speed, and it was more sensitive to depth of cut than to the variation of feed rate at the same cutting conditions, and RMSAE increases around 0.21mm flank wear hereby AE-HIT also increases. AE signals depend upon tool wear and fracture from the above results. Therefore, the AE signals can be utilized in order to monitor the tool condition.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2001.10a
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• pp.116-120
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• 2001

Fiber fracture is one of the dominant failure phenomena to determine total mechanical properties in composites. Fiber fracture locations were measured by optical microscopic method and acoustic emission (AE) as functions of matrix toughness and surface treatment by the electrodeposition (ED), and then two methods were compared. Two AE sensors were attached on the epoxy specimen and fiber fracture signals were detected with elapsed time. The interfacial shear stress (IFSS) was measured using tensile fragmentation test and AE system. In ED-treated case, the number of the fiber fracture measured by an optical method and AE was more than that of the untreated case. The signal number measured by AE were rather smaller than the number of fragments measured by optical method, since some fiber fracture signals were lost while AE detection. However, one-to-one correspondence between the x-position location by AE and real break positions by optical method was generally established well. The fiber break source location using AE can be a valuable method to measure IFSS for semi- or nontransparent matrix composites nondestructively (NDT).

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.3
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• pp.96-102
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• 2005

Acoustic Emission(AE) technique has been applied to not only material characterization evaluation but also on-line monitoring of the structural integrity. The AE source location technique is very important to identify the source, such as crack, leak detection. Since the AE waveforms obtained from sensors are very difficult to distinguish the defect signals, therefore, it is necessary to consider the signal analysis of the transient wave-form. In this study, we have divided the region of interest into a set finite elements, and calculated the arrival time differences between sensors by using the velocities at every degree from 0 to 90. A new technique for the source location of acoustic emission in fiberboard plates has been studied by introducing Wavelet Transform(WT) do-noising technique. WT is a powerful tool for processing transient signals with temporally varying spectra. If the WT de-noising was employed, we could successfully filter out the errors of source location in fiberboard plates by arrival time difference method. The accuracy of source location appeared to be significantly improved.

• Journal of the Korean Society for Heat Treatment
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• v.13 no.5
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• pp.309-317
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• 2000

Demand for new nondestructive evaluations is growing to detect tensile 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 investigated the strength of fire resistance steel for frame structure by tensile test after degradation treatment and analysed acoustic emission signals obtained from tensile test with time frequency analysis methods. In the T and TN specimens(under $600^{\circ}C$-10min ) consisting of ferrite and pearlite structure, most of acoustic emission events were produced near yield point, mainly due to the dislocation activities during the deformation. However, B specimen under $600^{\circ}C$-10min had a two peak which was attribute to the presence of martensite phase. The first peak is before yield point and the second after yield point. The sources of second acoustic emission peak were the debonding of martensite-martensite interface and the micro-cracking of brittle martensite phase. In $600^{\circ}C$-30min to $700^{\circ}C$-60min, many signals were observed before yield point and were decreased after yield point.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.14 no.6
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• pp.35-42
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• 2000

In this paper, AE(Acoustic Emission) signals detected from the growth of the electrical tree in an epoxy resin under ac high-voltage application were analysed to diagnose the insulation deterioration of cast-resin power transformer. Frequency spectra of AE signals generated from the magnetizing and the load currents in the actual operating cast-resin power transformer of 500[kVA] under distribution system of 22.9[kV] were also analysed to distinguish the AE signals due to void discharges from the magnetic circuit noises in the core of the transformer. As the experimental results, we could distinguish the AE signals whether those signals were caused due to the void discharges or due to the magnetic circuit noises by analyzing the frequency spectrum of AE signals. The frequency spectra of AE signals generated from the cast-resin power transformer in operation due to both the magnetizing and the load currents appeared in the range of 40-120[kHz], but the frequency band of AE signals emitted from the void discharges in an epoxy resin sample was about 50[kHz] to 230[kHz].

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.4
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• pp.425-433
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• 2001

A correlation between fatigue damage and acousto-ultrasonic (AU) parameters has been obtained from signals acquired during fatigue loading of the single-lap joints of a carbon-fiber reinforced plastic (CFRP) laminates and A16061 plate. The correlation showed an analogy to those representing the stiffness reduction $(E/E_0)$ of polymer matrix composites by the accumulation of fatigue damage. This has been attributed to the transmission characteristics of acoustic wave energy through bonded joints with delamination-type defects and their influence on the change of spectral content of AU signals. Another correlation between fatigue cycles and the spectral magnitude of acoustic emission (AE) signals has also been found during the final stage of fatigue loading. Both AU and AE can be applied almost in real-time to monitor the evolution of damage during fatigue loading.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.433-442
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• 2013

This paper presents experimental research work for the evaluation of one-dimensional and two-dimensional acoustic source locations with 50m length of a precast prestressed concrete box girder. Acoustic events are generated by the impacts of Schmidt Hammer and the impact signals are detected by acoustic emission sensors mounted on the concrete web surface of PSC box girder with the average spacing of 9.34m. Based on the amplitude of detected acoustic signals, considering the noises developed in PSC box girder bridges, the arrival times of acoustic signals are estimated by the first arrival times of 0Volt, 0.5Volt, and 1.0Volt amplitude in each signal. Using Least Square Method, the velocities and the source locations of acoustic signals are evaluated. Based on the test results, the spacing of AE sensors and the AE sensor networks are discussed to reduce the source location errors.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.20-27
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• 2000

A chatter vibration and a workpiece burn are the main phenomena to be monitored in modern grinding processes. This study describes a trouble diagnosis of the cylindrical plunge grinding process using the power and acoustic emission (AE) signals. The raw signals of the power and the AE occurred during the grinding operation were sampled and analyzed to determine the relationship between each fault and change of signals. A neural network that has a high success rate of the fault detection was used. Furthermore, an analysis on the influence of parameters to the chatter vibration and the grinding burn was conducted.

• Journal of the Korean Society for Nondestructive Testing
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• v.10 no.2
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• pp.23-31
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• 1990

Acoustic Emission(AE) signals obtained during fracture toughness test and fatigue test for nuclear pressure vessel material(SA 508 cl.3) and artificial AE signals from pencil break and ultrasonic pulser were classified using pattern recognition methods. Three different classifiers ; namely Minimum Distance Classifier, Linear Discriminant Classifier and Maximum Likelihood Classifier were used for pattern recognition. In this study, the performance of each classifier was compared. The discrimination of AE signals from cracking and crack surface rubbing was possible and the analysis for crack propagation was applicable by pattern recognition methods.