• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.11
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• pp.1143-1149
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• 2008

In a nuclear power plant, loose part monitoring and its diagnostic technique is one of the major issues for ensuring the structural integrity of the reactor system. Typically, accelerometers are mounted on the surface of a reactor vessel to localize impact location cavsed by the impact of metallic substances on the reactor system. However, in some cases, the number of the accelerometers is not enough to estimate the impact location precisely. In such a case, one of alternative plan is to utilize another type sensors that can measure the vibration of the reactor structure even though the measuring frequency ranges are different from each others. The AE sensors installed on the reactor structure can be utilized as additional sensors for loose part monitoring. In this paper, we proposed a new method to estimate impact location by using both accelerometer signal and AE signal, simultaneously. The feasibility of the proposed method is verified by an experiment. The experimental results demonstrate that we can enhance the reliability and precision of the loose part monitoring.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.5
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• pp.466-472
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• 2009

An acoustic leak monitoring system(ALMS) using acoustic emission(AE) technique was applied for leakage detection of nuclear power plant's pipeline which is operated in high temperature and pressure condition. Since this system only monitors the existence of leak using the root mean square(RMS) value of raw signal from AE sensor, the difficulty occurs when the characteristics of leak size and shape need to be evaluated. In this study, dual monitoring system using AE sensor and accelerometer was introduced in order to solve this problem. In addition, artificial neural network(ANN) with Levenberg.Marquardt(LM) training algorithm was also applied due to rapid training rate and gave the reliable classification performance. The input parameters of this ANN were extracted from varying signal received from experimental conditions such as the fluid pressure inside pipe, the shape and size of the leak area. Additional experiments were also carried out and with different objective which is to study the generation and characteristic of lamb and surface wave according to the pipe thickness.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.8
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• pp.863-867
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• 2014

In this study, an acoustic emission (AE) sensor was used for measuring the abrasive and molecular-scale phenomena in chemical mechanical polishing (CMP). An AE sensor is a transducer that converts a mechanical wave into an electrical signal, and is capable of acquiring high-level frequencies from materials. Therefore, an AE sensor was installed in the CMP equipment and the signals were measured simultaneously during the polishing process. In this study, an AE monitoring system was developed for investigating the sensitivity of the AE signal to (a) the variations in the material properties of the pad, slurry, and wafer and (b) the change in conditions during the CMP process. This system was adapted to Oxide and Cu CMP processes. AE signal parameters including AE raw frequency, FFT, and amplitude were analyzed for understanding the abrasive and molecular-level phenomena in the CMP process. Finally, we verified that AE sensors with different bandwidths could function in complementary ways during CMP process monitoring.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.4
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• pp.37-43
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• 2005

Real time monitoring of exit burr formation is critical in manufacturing automation. In this paper, acoustic emission (AE) was used to detect the burr formation during drilling. By using wavelet transform (WT), AE data were compressed without unnecessary details. Then the transformed data were used as selected features (inputs) of a back-propagation artificial neural net (ANN). In order to validate the in process AE monitoring system, both WT-based ANN and cutting condition (cutting speed, feed, drill diameter, etc.) based ANN outputs were compared with experimental data.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.4 no.2
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• pp.18-24
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• 1995

In order to achieve the autimation and untended system of manufacturing process, it is necessary that the monitoring system check up the disorder of machine tool or the conditions of tool wear for the maximum use of cutting tool. In the metal cutting Process, AE signal is detected by AE sensor, then amplified and transmitted to an Locan-AT. The experiment was performed to SM25C and STS304 steels at uniform feedrate, cutting speed and depth of cut, The results of experimental data apparently showed emission intensity vary due to increasing of tool wear at the 165kHz, 200kHz in the SM25C and 140kHz, 165kHz, 200kHz, in the STS304 respectively Therefore, it is possible to predict the tool wear. This study is intended to suggest the way to the automation and untended system of machine tool through the system monitoring tool wear by using AE signal.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.3
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• pp.205-211
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• 2003

Acoustic emission (AE) technique has been applied to not only mechanical property testing but also on-line monitoring of the el)tire structure or a limit zone only. Although several AE devices have already been developed for the on-line monitoring, the price of these systems is very high and it is difficult for the field to apply yet. In this study, wc developed a specially designed PC-based source location system using the A/D board. The source location technique is very important to identify the source, such as crack, leak detection. However, since the AE waveforms obtained from transducers are very difficult to distinguish the defect signals, therefore, it is necessary to consider the signal analyses of the transient waveform. Wavelet Transform (WT) is a powerful tool for processing transient signals with temporally varying spectra that helps to resolve high and low frequency transients components effectively In this study, the analyses of the AE signals are presented by employing the WT analyses. AE results are compared the PC-based source location system using A/D board with the commercial AE system.

• Journal of the Korean Society for Nondestructive Testing
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• v.16 no.3
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• pp.155-161
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• 1996

For the condition monitoring of the journal bearing in rotating machinery, a system for their diagnosis by acoustic emission(AE) was developed. AE has been used to detect abnormal conditions in the bearing system. It was found from the field application study as well as the laboratory experiment using a simulated journal bearing system that AE RMS voltage was the most efficient parameter for the purpose of current study. Based on the above results, algorithms and judgement criteria for the diagnosis system was established. The system is composed of four parts as follows: the sensing part including AE sensor and preamplifier, the signal processing part for RMS-to-DC conversion to measure AE ms voltage, the interface part for transferring RMS voltage data into PC using A/D converter, and the software part including the graphic display of bearing conditions and the diagnosis program.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.4
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• pp.100-110
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• 1995

This paper aims at reviewing the possibility of application over normal or abnormal, detection used by AE, and the characteristics of grinding processes. In this study, when WA-vitri-fied ' resinoid bond grinding wheels:36 kinds of grinding wheel and grinding depth were tuned at the surface grinding, the zone of AE signal generation is theoretically modelled and reviewed by grinding processes. The variation of grinding resistance( F$n^{9}$ $F_{t}$) and AE signal is detected in-process by the use of AE measuring system. The tests are carried out in accordance with grain size and grade of grinding wheels, and work-pieces-STD11 and STD61. According to the experiment's results, the following can be expected;as grinding time passes by, the relation of grinding depth and quantity of AE signal, observing on AE signal and grinding burn suggest the characteristics of grinding processes and evalution on the possibility of control of grinding machine, and monitoring abnormal conditions.e, and monitoring abnormal conditions.

• Journal of Korean Tunnelling and Underground Space Association
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• v.16 no.5
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• pp.471-485
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• 2014

The monitoring method of geotechnical structures using acoustic emission(AE) and microseismicity(MS) is to detect the microscopic deformation and fracture behavior in the inner structures by measuring induced acoustics and vibrations. It can identify a pre-indication of failure by taking advantage of the characteristics that the amount and occurrence rates of AE and MS increase rapidly prior to large scale destruction of the target structures. The monitoring system consisting of high-quality sensors, high-speed data acquisition device and the operation program is required for the practical application of this method. Recently, the AE and MS monitoring systems have been localized. In particular, the developed operation software which can analyze and interpret the measured signals was demonstrated through a number of applications to domestic fields. This report introduces the configuration and features of developed monitoring system, then the challenges and future direction of AE monitoring in geotechnical structures are discussed.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.108-115
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• 1999

Since grinding is a more complicated process than any other machining process, it is hard for operators to setup a grinding machine properly and to find out correctly abnormal grinding states resulting in damages to products. Abnormalities would be caused by improper setup, improper dressing/grinding conditions which are likely to be occurred without skilled operators' attention. In this study, an AE monitoring unit is developed to help operators conduct with ease setup, and set properly dressing/grinding conditions. AErms(root-mean-square) signal being monitored, on-going process states during grinding and dressing is visualized for machine operators to judge whether the processes are in good condition. Evaluation tests are carried out on centerless grinding machines-both cylindrical and internal. The developed AE monitoring system is verified to be useful to check grinding/dressing states in process even in the centerless grinding of which process is most unknown among various grinding methods because of the complex structure.