• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.2
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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.

• Composites Research
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• v.23 no.6
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• pp.39-46
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• 2010

Acoustic emission (AE) signals are detected during the polymerization shrinkage of composite resin restoration in artificial dental ring according to various interfacial treatment conditions. AE amplitudes and the number of AE hit events were compared through the non-parametric statistics of Mann-Whitney method and Kruskal-Wallis method. The AE amplitudes detected from the PMMA and human tooth ring specimens were not significantly different according to adhesive conditions. The stainless steel ring specimen, meanwhile, had a difference in AE amplitude (p<0.05). The quantity of hit events for the human molar dentin specimens of the good bonding state was much less than that for the steel ring specimen but more than that for the PMMA ring specimen. For the same substrate, the better the bonding state, the less the AE hit events (p<0.05). The degree of marginal disintegration measured by SEM was proportional to the amount of AE hit events detected.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.656-659
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• 1993

The failure phenomenon of Dual Basalt Fibers Reinforced Epoxy Composites(DFC) under tensile load was studied using acoustic emission(AE) technique. AE amplitude and AE energy were mainly associated with the internal microscopic failure mechanism of DFC specimen, such as fiber fracture, matrix cracking, and fiber/matrix debonding. Fiber failures in the DFC specimens were distinguishable by showing the highest AE energy amplitude. They were dependant on the fiber diameters. Matrix cracking was determined from the relatively lower AE amplitude and AE energy, whereas fiber/matrix debonding could not be successfully isolated. AE method, however, can be applicable to the fragmentation method for interfacial strength(IFSS) in DFC specimens with adjusting the threshold to isolate fiber breaks from matrix crack and debonding.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.04a
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• pp.582-587
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• 2011

Acoustic Emission(AE) is widely used for early detection of faults for rotating machinery in these days because of its high sensitivity. AE signal has to need for transferring to low frequency range for the spectrum analysis included the fault mechanism. In transferring process, we lose a lot of fault information caused by unusable signal processing method. Discrete Wavelet Transform(DWT) is a method of signal processing for AE signatures, but the pattern of its mother function is not optimized with AE signals. So, we can lose the fault information when we want to use the DWT for AE signal. Therefore, in this paper, we will propose a novel pattern for DWT mother function, which is optimized with AE signals. And it will be applied to compare the results of DWT by daubechie and novel pattern.

• 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 the Korean Society for Precision Engineering
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• v.8 no.4
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• pp.118-125
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• 1991

Acoustic microscopy has attracted much interest recently as potential nondestructive evaluation technique for detecting and sizing defects of surface and sub-surface. Also acoustic emission testing method has been developed for detecting microcracks which is more than 30${\mu}m$ in length quantitatively on ceramics. In the present paper, acoustic emission during the four point bending test in hot-pressed sintered $Si_3N_4$ specimen which was stressed by thermal shock, has been measured by high sensitive sensing system. The surface and sub-surface cracks were detected by scanning acoustic micrscope of 800 MHz and conventional ultrasonic testing in C-scope image. The purpose was to investigate the location and size of cracks by SAM and AE technique, whose experimental data demonstrate good for detecting microcracks.

• 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 of Manufacturing Process Engineers
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• v.9 no.3
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• pp.76-82
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• 2010

A laborer always watched a process of production carefully but defective manufactures were inspected after press process. These inspections made a waste of human power and defective manufactures could make a serious damage of press mold. Therefore, AE(Acoustic Emission) system was introduced to prevention of the damage of the press molds, to a real time detection of defective manufactures and to save human power. AE system was introduced to solve this problem which is a detecting defective manufacture on real time and to prevent the damage of the press mold. In this research we get acoustic emission signal in accordance with weight and processing method of press by using AE sensor, Preamplifier, AE board signal board which occurs press processing and it analyzed various signal through using CMD8 software on the time. From the result, we found that the intensity and shape of the signal were changed according to the weight and processing type of the press. By using this special algorithm, it can judge the acoustic signal which occurs from press on real time.

• Smart Structures and Systems
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• v.18 no.3
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• pp.525-540
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• 2016

The CFRP-confined circular concrete-filled steel tubular column is composed of concrete, steel, and CFRP. Its failure mechanics are complex. The most important difficulties are lack of an available method to establish a relationship between a specific damage mechanism and its acoustic emission (AE) characteristic parameter. In this study, AE technique was used to monitor the evolution of damage in CFRP-confined circular concrete-filled steel tubular columns. A fuzzy c-means method was developed to determine the relationship between the AE signal and failure mechanisms. Cluster analysis results indicate that the main AE sources include five types: matrix cracking, debonding, fiber fracture, steel buckling, and concrete crushing. This technology can not only totally separate five types of damage sources, but also make it easier to judge the damage evolution process. Furthermore, typical damage waveforms were analyzed through wavelet analysis based on the cluster results, and the damage modes were determined according to the frequency distribution of AE signals.

• 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.