• Journal of Ocean Engineering and Technology
• /
• v.10 no.2
• /
• pp.69-76
• /
• 1996

In-process quality control and high reliability of the weld are the major concerns in applying friction welding to the economical and qualified mass-production. Experimental examinations and quantitative analysis were performed for the optimiaztion of similar friction welding of hydraulic valve spool steels(SNCM220, SCM435, SACM645, SCM415, ${\varphi}24). The quantitative correlations were found between the initial cumulative counts of acoustic emission(AE) occurring during plastic deformation periods of the welding and the fatigue life as well as weld strength and welding conditions. A real-time evaluation system was developed for the friction weld quality by AE.

• Journal of Power System Engineering
• /
• v.11 no.1
• /
• pp.39-44
• /
• 2007

The purpose of this study is to verify availability of the acoustic emission in-situ monitoring method to the internal leak and operating conditions of the major valves at nuclear power plants. In this study, acoustic emission tests are performed when the pressurized temperature water and steam flowed through glove valve(main steam dump valve) and check valve(main steam outlet pump check valve) on the normal size of 12 and 18". The valve internal leak monitoring system for practical field was designed. The acoustic emission method was applied to the valves at the site, and the background noise was measured for the abnormal plant condition. To improve the reliability, a judgment of leak on the system was used various factors which are AE parameters, trend analysis, frequency analysis, voltage analysis and amplitude analysis of acoustic signal emitted from the valve operating condition internal leak.

• Journal of the Korean Society for Precision Engineering
• /
• v.22 no.4
• /
• pp.60-67
• /
• 2005

Compared with conventional welding, laser spot welding offers a unique combination of high speed, precision and low heat distortion. This combination of advantages is attractive for manufacturing industries including automotive and electronics companies. In this paper, a real time monitoring scheme fur a pulsed Nd:YAG laser spot welding was suggested. Acoustic emission (AE) signals were collected during welding and analyzed for given process conditions such as laser power and pulse duration. A back propagation artificial neural network, with AE frequency content inputs, was used to predict the weldability of stainless steel sheets.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2008.11a
• /
• pp.43-48
• /
• 2008

Gear system has been widely used in industrial applications and unexpected failures of gears are not only extremely damaging but also lead to economic losses. So, early detection of fault is important for diagnosis machine condition. And acoustic emission is an efficient non destructive testing technique for the diagnosis of machine health and is useful technique for early detection of fault because it can find low-amplitude and high-frequency signal on account of high sensibility. Therefore, in this paper, the AE signal was measured and preprocessed using envelop analysis for gearbox with misalignment between pinion and gear. And then the vibration characteristic of gear misalignment was analyzed.

• Journal of the Korean Society for Precision Engineering
• /
• v.25 no.10
• /
• pp.41-47
• /
• 2008

This study aims to obtain machining characteristics during AFM (Atomic force Microscope) machining of silicon wafers and to monitor the machining states using acoustic emission. As in micro scale machining, two distinct regimes of deformation, i. e. ploughing regime and cutting regime were observed. First, the transition between the two regimes are investigated by analyzing the "pile-up" during machining. As far as in process monitoring is concerned, in the ploughing repime, no chips have been formed and related AE RMS values are relatively low, In the mean time, in the cutting regime, the RMS values are significantly higher than the ploughing regime, with apparent chip formation. From the results, we found out that the proposed scheme can be used for the monitoring of nanomachining, especially for the characterization of nanocutting mode transition.

• Journal of the Korean Society for Precision Engineering
• /
• v.19 no.2
• /
• pp.133-139
• /
• 2002

A number of experimental studies on burr formation in face milling operations have been pursued. They usually focus on machining parameters such as depth of cut, leed rate, spindle speed and in-plane exit angle. But it if difficult to set the correlation between burrs and the parameters on burr when such parameters are considered at the same time. Therefore, in this paper, acoustic emission (AE) is considered as an alternate way to predict burr types regardless of machining conditions. AE signals during face milling were sampled and processed, then fed into an artificial neural network to classify burr types \\\"on-line\\\".\\\".uot;.

• Journal of the Korean Society for Precision Engineering
• /
• v.17 no.4
• /
• pp.240-246
• /
• 2000

An in-process estimation of the ground surface roughness is a bottle-neck and an essential field in conventional grinding operation. We defined the dimensionless average roughness factor (D.A.R.F) that exhibits a roughness characteristics of ground surface. The D.A.R.F was composed easily of the absolute average and the standard deviation values which were the analytic parameters of the acoustic emission (AE) signal generated during the machining process. The theoretical equation between the surface roughness and the D.A.R.F has been derived from the linear regressive analysis and verified its availability through the experimentation on the surface grinding machine.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2007.05a
• /
• pp.905-908
• /
• 2007

The purpose of this work is to obtain fundamental data about fatigue crack detection of the welded exhaust flange by using the AE method. The acoustic emission method as a nondestructive evaluation is one of high technical test for realtime monitoring in the dangerous industry fields. Signal analysis of both AE sensor and accelerometer for fatigue crack failure are presented in this paper.

• Proceedings of the Korea Concrete Institute Conference
• /
• 1999.04a
• /
• pp.543-546
• /
• 1999

The purpose of the present study is to identify the damage characteristics of concrete structures due to cracking by employing the acoustic emission techniques. A comprehensive experimental study has been done. The cracking damages under tensile and flexural loadings have been identified and the bond damage between steel and concrete have been also characterized. It is seen that the amplitudes and energy level of AE events is found to be smaller for bond cracking damages and larger for tensile cracking damages. The characteristic equations of the AE events for various cracking damages have been proposed based on the present test data. The internal microcracks are progressively developed ahead of a visible actual crack and the present study clearly exhibits thses damage mechanism for various types of cracking in concrete. The present study provides very useful data which can be used to identify the various types of cracking damages in concrete structures. This will allow very efficient maintenance of concrete structures through monitoring of internal cracking based on acoustic emission.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2003.10a
• /
• pp.283-288
• /
• 2003

The object of this study is to investigate a fracture characteristics of static tensile test as a function of acoustic emission according to the fiber orientation $\theta=0^{\circ}C$ in carbon/epoxy composites, CFRP. On tensile loading, it was recognized that the fracture characteristics of CFRP in a unidirectional composites. Using the results of the AE analysis(a=2mm), it was found that the amplitude distributions of AE signals corresponding the matrix cracking, fiber debonding or delamination, and fiber breaking are 55~70dB(<200sec), 100dB(200~600sec), and 80dB(>600sec).