• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.10a
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• pp.344-348
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• 1992

This paper presents a new methodology for on-line tool breakage detection by sensor fusion concept of an acoustic-emission (AE) sensor. A built-in piezoelectric force sensor was used to measure cutting force instead of a tool dynamometer to preserve the machine tool dynamics. he sensor was inserted in the tool turret housing of an NC lathe. FEM analysis was carried out to locate the most sensitive position for the sensor. When a tool is broken, the explicit changes of signals' pattern take place. A burst-type AE signal increases abruptly. Followingly, a cutting force drops significantly. Therefore a burst of AE signal is used as a triggering signal to inspect the following cutting force. Significant drop of cutting force is utilized to detect tool breakage. The algorithm was implemented in a DSP board for in-process tool breakage detection. The proposed monitoring system was capable of a good applicable tool breakage detection.

• International Journal of Precision Engineering and Manufacturing
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• v.7 no.3
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• pp.14-17
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• 2006

To diagnosis abnormal compressor conditions in an air-conditioner, the acoustic emission (AE) signal, which is derived from wear condition, compressed air, and assembly error, was analyzed experimentally. Burst and continuous type AE signals resulted from metal contact and compressed air, and the raw AE signal of compressors was acquired in the production line. After extracting samples using waveforms, the Early Life Test (ELT) was conducted and the waveform was classified as normal or abnormal. Efficient parameters in the waveform pattern were investigated in time and frequency domains and a diagnosis algorithm for air-conditioners using Neural Network estimation is suggested.

• Tribology and Lubricants
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• v.17 no.3
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• pp.244-250
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• 2001

A rotating axis diagnostic system supported with journal bearing has been established that has been widely used in the industry. In order to measure the most sensitive signals that would be generated in the abnormal operation, sensors which measure AE(acoustic emission), gap and acceleration have been attached at the various location on the experimental apparatus. Data were obtained in the steady state operational condition of the system which was verified through the empirical measurement. Notable discrepancies were observed in RMS acceleration signal which could be utilized to predict the undesirable operational condition of the system.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.17 no.5
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• pp.567-571
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• 2004

Recently, GIS(Gas Insulated Switchgear) has been recognizing of importance on development of diagnosis technique which is happened problem on confidence for a long time use. Therefore, the measurement and analysis of PD with prior phenomenon of insulation breakdown is used many method of diagnosis for GIS. In this paper, we simulate trouble condition in DS and analysis trouble signal to use electrical and mechanical methods, interpretation of detected signal has analysed with to use ø-q-n pattern and neural network. For this analysis, we have used the induction and AE(acoustic emission) sensors. For the simulation experiment, we make DS for 170 KV GIS and analyze the classification and characteristics of detected signals with the application of neural network algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.131-137
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• 1994

Detection of grinding trouble occuring during the grinding process is classified into two types, i.e, based on the quantitative and qualitative knowledge. But, since the grinding operation is especially related with a large amount of functional parameters, it is actually defficult to cope with the grinding troubles occuring during process. Therefore, grinding trouble-shooting has difficulty in satisfying the requirement from the user. To cope with the grinding troubles occuring during the process, the application of neural network is on effective way. In this study, we identify the four parameters derived from the AE(Acoustic Emission) signals and present the grinding trouble-shooting system utilizing a back-propagation model of the neural network.

• 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 for Precision Engineering
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• v.12 no.12
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• pp.100-108
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• 1995

The In-process minitoring of the chatter vibration is necessarily required to an automatic manufacturing system. In this study, we constructed a multi-sensing system using tool dynamoneter, accelerometer and AE(Acoustic Emission) sensor for a more credible detection of chatter vibration. And a new approach using a multiple neural network to extract the features of multi-sensor for the recognition chatter vibration is proposed. With the Back-propagation training process, the neural network memorize and classify the features of multi-sensor signals. As a result, it is shown by multiple neural network that the chatter vibration can be monitored accurately, and it can be widely used in practical unmanned system.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.2
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• pp.75-80
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• 2006

In this paper, to detect and observation the void discharges pulse signal, AE signals and tree growth characteristics in case the high voltage is applied to a XLPE sample for a power cable. We also examined the partial discharge current pulse and AE signals with the increase of the applied voltage in XLPE insulation. The experimental results show that a branch-type tree grows in the presence of the voids, and a bush-type tree grows in the absence of the voids in both samples. A rate of tree growth increases abruptly in proportional to the deterioration time in the presence of the of the voids, but in the absence of the voids, a rate of tree growth decreases as time goes by and finally a breakdown occurs. The frequency band of AE signals that are generated from the partial discharges in a XLPE sample, one of solid dielectric materials, is about 1.0[MHz].

• Journal of Ocean Engineering and Technology
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• v.19 no.5 s.66
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• pp.43-49
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• 2005

This paper is to investigate the effect of corrosion by acoustic emission method in tensile loading and the adhesiveness between substrate and coating layer. The powders used are Zn and Amdry625, respectively. They are coated on brass alloy substrate. AE signals of Zn and Amdry625 coating layer increase drastically in strain $2\%$. However, those of Zn specimen have more than those of Amdry625 specimen. When the specimens executed the corrosion test under $3.5\%$ NaCl solution for 500, 1000 hours, the salt solution penetrated into the surface of the substrate through the pores of the coating layer. As a result, corrosion production formed on the surface of the substrate. The adhesiveness between coating layers is weakened by the polarization and corrosion itself. The AE event, count, and energy of corroded coating specimens decrease, compared to specimens without corrosion. The results are summarized as follows : 1. In the tensile tests, the time that it took to start and develop the cracks and exfoliations between the surface of the substrates and the plasma spray coatings were different according to the type of plasma sprayed material, which are Zn and Amdry625. These phenomena were obvious at the strain rate 1 to $5\%$, and few available data were found after that stage. 2. The specimens with Zn coating showed the characteristics of crack, according to the changes of the tensile strength applied on the substrates while those with Amdry625 showed exfoliation as a result of low adaptation to the tensile strength. 3. The anti-corrosion specimens showed that the adhesive properties between the substrate and the plasma spray coating were strong in the order of Zn, Amdry. It showed that Corroded specimens cracked or exfoliated easily, even with the small energy, because those had a comparatively weakened adhesive property, due to corrosion. 4. Zn specimen showed no corrosion phenomena on the surface of the substrates, because they had the function of sacrifice anode however, Amdry625 specimen showed the corrosion, because it did not have that function.