• Journal of the Korean Society for Precision Engineering
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• v.13 no.4
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• pp.136-142
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• 1996

Automatic monitoring of cutting process is one of the most important technology in machining. AE sensing technology has been applied to monitoring process and proved to be effective in detecting tool abnor- malities such as tool wear and fracture. In this experimental study. AE signals were detected from the tool holder for continuous and interrupted cutting, which obtained from changing workpice material configuration, under control of constant cutting speed from CNC lathe. From statistical and frequency analysis, the AE signals were analyzed to obtaining the characteristics of continuous and interrupted cutting conditions and tool failure. The Kurtosis values decreased but RMS voltages increased as the cutting speed increased, in both continuous and interrupted cutting. RMS voltage is suddenly increased but Kurtosis value is suddenly decreased when tool failure condition. Power spectrum density of AE signals when tool failure reaches extreme value around 0.065 cycles/ .mu. m.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.8-13
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• 2000

Die polishing technology is very critical to determine quality and performance of the final products. Die polishing processes have not been automated because the automation requires a great deal of experience and skill of experts. Thus, to implement a fully automated polishing process, the development of polishing status monitoring system replacing the skill of experts is critical. AE is known to be closely related to material removal rate(MRR). As the surface is rougher, MRR gets larger and AE increased. The surface roughness can be indirectly estimated using the AE signal measured during automatic die polishing process. In this study, The polishing state monitoring system using AEms signal was developed. This system can be not only to monitor the abnormal state but also to estimate a state of surface roughness of polishing surface qualitatively.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.3
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• pp.14-21
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• 1998

The in-process detection of the state of cutting tool is one of the most important technical problem in Intelligent Machining System. This paper presents a method of detecting the state of cutting tool in turning process, by using Artificial Neural Network. In order to sense the state of cutting tool. the sensor fusion of an acoustic emission sensor and a force sensor is applied in this paper. It is shown that AErms and three directional dynamic mean cutting forces are sensitive to the tool wear. Therefore the six pattern features that is, the four sensory signal features and two cutting conditions are selected for the monitoring system with Artificial Neural Network. The proposed monitoring system shows a good recogniton rate for the different cutting conditions.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.3
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• pp.394-400
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• 2012

Due to the increasing demand for carious methodologies, the quality improvement of products were introduced. A brush, the most frequently used type of grinding process, is one of the deburring. In order to produce consistent burr shape, various machining conditions have been combined and applied to disk grinding process. By tool dynamometer, acoustic emission sensor and acceleration sensor depend on changes in processing conditions(depth of engagement, cutting speed, workpiece position, workpiece orientation, cutting time) signals were obtained for brush deburring grinding system. Root mean square obtained by processing the signal processing conditions by analyzing the characteristics of deburring is to derive the optimum conditions.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2005.10a
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• pp.186-189
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• 2005

This paper was designed to assess the adhesive properties of hard coatings on non-nitrided and nitrided various tool steels. Estimations of adhesion were done to scratch test which is mainly used in hard coating. The critical load(Lc) between coating and substrate is defined through analysis of frictional load vs. normal load curve, signals of acoustic emission and optical observations. Coatings employed in this study are TiN, CrN and TiAlN, tools as substrates are STD11, STD61 and SKH51. It was classified to substrates with/without nitrided layer and hard coatings on substrate were deposited by arc PVD. Results showed that harder substrates and coatings give higher values of critical loads.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.33 no.4
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• pp.285-289
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• 1997

A Held experiment was carried out to confirm the effect of underwater sound on the luring of fish school of rudder fish in a set net at the coast of Cheju Island. The effects of the acoustic emission on the luring of fish school were observed at a cage around a set net fishing ground using a manufactured underwater speaker. Underwater sounds that were emitted for the luring of fish school were the pure sounds of which frequency were 300Hz and 400Hz, engine noise and swimming sound. The results of the observation are as follows : 1. The input and output wave forms of a manufactured underwater speaker in water tank were similar to those in measurement frequencies. The result of the observation indicated that it could be used for the purpose of the sound emission in measurement frequencies. 2. The effect of the emitted pure sound of 300Hz, 400Hz was remarkable for the luring of fish school in 2 minutes after the sound emission. The reaction of fish school was more sensitive to the pure sound of 400Hz than 300Hz. 3. The effect of the emitted engine noise was more remarkable than that of the pure sound for 3 minute continuously. On the feeding sound, fish formed a shoal and swimmed, but didn't gather around the underwater speaker. 4. The feeding and swimming sound spectra on rudder Hsh showed similar sound pressure distribution each other, they appeared low sound pressure in frequencies of 200-600Hz.

• Geomechanics and Engineering
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• v.20 no.2
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• pp.113-120
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• 2020

Rock damage is the main cause of accidents in underground engineering. It is difficult to predict rock damage accurately by using only one parameter. In this study, a rock failure prediction model was established by using stress, energy, and damage. The prediction level was divided into three levels according to the ratio of the damage threshold stress to the peak stress. A classification predicting model was established, including the stress, energy, damage and AE impact rate using Bayesian method. Results show that the model is good practicability and effectiveness in predicting the degree of rock failure. On the basis of this, a multi-parameter classification predicting deterioration model of rock failure was established. The results provide a new idea for classifying and predicting rockburst.

• Journal of the Korean Wood Science and Technology
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• v.31 no.2
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• pp.84-91
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• 2003

In this study, the three species (Populus euramericana, Pinus densiflora and Quercus variabilis) were cut to difference (0, 0.15, 0.3, 0.45 mm) between the size of tip and that of root of the finger (DSTR) and jointed with poly vinyl acetate (PVA) and resorcinol-phenol resin (RPR). We described the relationship between the bending strength properties of finger DSTR and the acoustic emission (AE) generated during the bending test. The results were as follows: The AE generation time of finger-jointed specimens with RPR adhesive was earlier than that with PVA adhesive. The AE cumulative event count of finger-jointed specimens with RPR adhesive continuously increased with increasing load and the event count was much more than that with PVA adhesive. Also, the AE cumulative event count for resorcinol-phenol resin adhesive obtained from low load level was abundant. The AE wave in finger-jointed specimens with RPR adhesive could be detected in the below proportional limit load. Therefore, AE signals from bending test are useful for the estimation of strength in finger DSTR specimens.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.8
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• pp.57-64
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• 2004

This study deals with the prediction of defect location which can be occurred in structure. The existing methods was very difficult to be applied to predict it, because of complex numerical formula. The triangulation method proposed in this study can predict the source location easily with small amount of data. The arrival time of wave can be directly converted into the distance between sensors. For this purpose, the propagation velocity was measured by Rayleigh wave, and the propagation behavior was analyzed. The welded workpiece is adapted to investigate for the consideration of jointed part in structure, The propagation velocity of signal was measured in welded workpiece and the revised algorithm of source location was proposed.

• Journal of Ocean Engineering and Technology
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• v.10 no.3
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• pp.24-33
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• 1996

Up to now, most of studies on mechanical properties in friction welded components are about tensile and bending strength. However the fatigue studies on the friction-welded components subjected to repeated stress are not available. The purposes of this study are the development of fundamental design and the development of in-process real-time weld quality evaluation technique by acoustic emission for the bar-to-bar dissimilar friction welding of hydraulic valve spool steels.