• Proceedings of the Safety Management and Science Conference
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• 2008.04a
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• pp.185-196
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• 2008

The objective of this study is to evaluate the mechanical behaviors and structural integrity of the weldment of high strength steel by using an acoustic emission (AE) techniques. Simple tension and AE tests were conducted against the 3 kind of welding test specimens. In order to analysis the effectiveness of weldability, joinability and structural integrity, we used K-means clustering method as a unsupervised learning pattern recognition algorithm for obtained multivariate AE main data sets, such as AE counts, energy, amplitude, hits, risetime, duration, counts to peak and rms signals. Through the experimental results, the effectiveness of the proposed method is discussed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.678-682
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• 1996

Acoustic emission(AE) is monitored during end milling with a sensor attached to the cutting fluid discharge nozzle. Cutting forces are also measured and compared with the AE signals to examine the reliability of the AE signals. It is demonstrated that the AE signals provide reliable informations about the cutting processes and tool states. Moreover, edge chipping and fracture of tools can be successfully detected using both the AE signals and cutting forces.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.20 no.3
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• pp.218-222
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• 2007

In this study, Acoustic emission(AE) sensors were fabricated using lead-free piezoelectric ceramics for prohibiting environmental pollution. Structure of AE sensors were designed as Langvin type air backing form. Here, the piezoelectic element was used as PZT(EC-65)(AE1) and NKN(AE2), respectively. The measured resonant frequency, the maximum sensitivity frequency and sensitivity of AE sensors were as follows ; 143 kHz, 29.4 kHz and 69.3 dB in AE1 and 179 kHz, 29.4 kHz and 66.3dB in AE2, respectively.

• Tunnel and Underground Space
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• v.18 no.1
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• pp.1-9
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• 2008

Acoustic emission (AE) and Microseimsic (MS) activities are law-energy seismic events associated with a sudden inelastic deformation such as the sudden movement of existing fractures, the generation of new fractures or the propagation of fractures. These events rapidly increase before major failure and happen within a given rock volume and radiate detectable seismic waves. The main difference between AE and MS signals is that the seismic motion frequencies of AE signals are higher than those of MS signals. As the failure of geotechnical structures usually happens as a high velocity and small displacement, it is nat easy ta determine the precursor and initiation stress level of failure in displacement detection method. To overcame this problem, AE/MS techniques far detection of structure failure and damage have recently adapt in civil engineering. This study deal with the basic theory of AE/MS and state of arts in monitoring technique using AE/MS.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.2
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• pp.258-266
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• 2012

Recently, Acoustic Emission (AE) technique is widely applied to develop the early fault detection system, and the problem about a signal processing method for AE signal is mainly focused on. In the signal processing method, envelope analysis is a useful method to evaluate the rolling element bearing problems and Wavelet transform is a powerful method to detect faults occurred on gearboxes. However, exact method for AE signal is not developed yet. Therefore, in this paper, two methods, which is Hilbert transforms (HT) and Hilbert-Huang transforms (HHT), will be compared for development a signal processing method for early fault detection system by using AE. AE signals were measured through a fatigue test. HHT has better advantages than HT because HHT can show the time-frequency domain result. But, HHT needs long time to process a signal, which has a lot of data, and has a disadvantage in de-noising filter.

• Composites Research
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• v.22 no.6
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• pp.45-51
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• 2009

The marginal integrity at the composite resin-tooth interface has been analyzed in real time through acoustic emission (AE) monitoring during the polymerization shrinkage of composite resin subjected to the light exposure. It was found that AE signals were generated by the polymerization shrinkage. Most AE hit events showed a blast type signal having the principal frequency band of 100-200kHz. Bad bonding states were indicated by many hit events in the initial curing period of 1 minute with high contraction rate. The quantity of hit events for the human molar dentin specimen was much less than that for the steel ring specimen but more than that for the PMMA ring specimen. The better the bonding state, the less the AE hit events. The AE characteristics were related with the tensile crack propagation occurring in the adhesive region between the composite resin and the ring substrate as well as the compressive behavior of the ring substrate, which could be used for a nondestructive characterization of the marginal disintegrative fracture of the dental restoration.

• Transactions of the Korean Society of Mechanical Engineers
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• v.10 no.6
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• pp.906-915
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• 1986

This study reviewed the theory of acoustic emission applying generation of acoustic emission in metal cutting and proposed a relationship between fundamental cutting parameters and the root mean square (RMS) voltage of the acoustic emission on the basis of the mechanics of the orthogonal cutting operation. Experimental results are presented for 6063-T5 Auminum and the validity of this relationship is evaluated by a series of tests varying cutting speed, feed rate and rake angle in orthogonal cutting. The original formula derived theoretically has been modified in order to utilize independent of fundamental cutting parameters. RMS voltage of acoustic emission depends on cutting speed and strain rate, but it is not much affected by feed rate. Applying lubricant, the drop of RMS level has been observed.

• Smart Structures and Systems
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• v.18 no.5
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• pp.1063-1085
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• 2016

Taking advantage of the high sensitivity and long-distance detection capability of acoustic emission (AE) technique, this paper focuses on the crack detection in rail head, which is one of the most vulnerable parts of rail track. The AE source location and noise cancellation were studied on the basis of practical rail profile, material and operational noise. In order to simulate the actual AE events of rail head cracks, field tests were carried out to acquire the AE waves induced by pencil lead break (PLB) and operational noise of the railway system. Wavelet transform (WT) was first utilized to investigate the time-frequency characteristics and dispersion phenomena of AE waves. Here, the optimal mother wavelet was selected by minimizing the Shannon entropy of wavelet coefficients. Regarding the obvious dispersion of AE waves propagating along the rail head and the high operational noise, the wavelet transform-based modal analysis location (WTMAL) method was then proposed to locate the AE sources (i.e. simulated cracks) respectively for the PLB-induced AE signals with and without operational noise. For those AE signals inundated with operational noise, the Hilbert transform (HT)-based noise cancellation method was employed to improve the signal-to-noise ratio (SNR). Finally, the experimental results demonstrated that the proposed crack detection strategy could locate PLB-simulated AE sources effectively in the rail head even at high operational noise level, highlighting its potential for field application.

• Composites Research
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• v.24 no.3
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• pp.25-30
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• 2011

The studies on the non-destructive testing methods of the composite materials are very important for improving their reliability and safety. AE(Acoustic Emission) can evaluate the defects by detecting the emitting strain energy when elastic waves are generated by the generation and growth of a crack, plastic deformation, fiber breakage, matrix cleavage or delamination. In this paper, the AE signals of the filament wound composite cylinder and sandwich cylinder during the pressure test were measured and analyzed. The signal characteristics of PVDF sensors were measured, and an AE signal analyzer which had the band-pass filter and L-C resonance filter were designed and fabricated. Also, the crack detection capability of the fabricated AE signal analyzer wes evaluated during the pressure tests of the filament wound composite cylinder and the sandwich cylinder.

• Smart Structures and Systems
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• v.6 no.3
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• pp.197-209
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• 2010

The structural state of a bridge is currently examined by visual inspection or by wired sensor techniques, which are relatively expensive, vulnerable to inclement conditions, and time consuming to undertake. In contrast, wireless sensor networks are easy to deploy and flexible in application so that the network can adjust to the individual structure. Different sensing techniques have been used with such networks, but the acoustic emission technique has rarely been utilized. With the use of acoustic emission (AE) techniques it is possible to detect internal structural damage, from cracks propagating during the routine use of a structure, e.g. breakage of prestressing wires. To date, AE data analysis techniques are not appropriate for the requirements of a wireless network due to the very exact time synchronization needed between multiple sensors, and power consumption issues. To unleash the power of the acoustic emission technique on large, extended structures, recording and local analysis techniques need better algorithms to handle and reduce the immense amount of data generated. Preliminary results from utilizing a new concept called Acoustic Emission Array Processing to locally reduce data to information are presented. Results show that the azimuthal location of a seismic source can be successfully identified, using an array of six to eight poor-quality AE sensors arranged in a circular array approximately 200 mm in diameter. AE beamforming only requires very fine time synchronization of the sensors within a single array, relative timing between sensors of $1{\mu}s$ can easily be performed by a single Mote servicing the array. The method concentrates the essence of six to eight extended waveforms into a single value to be sent through the wireless network, resulting in power savings by avoiding extended radio transmission.