• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.888-891
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• 2000

Drilling is one of the most important operations in machining industry and usually the most efficient and economical method of cutting a hole in metal. From automobile parts to aircraft components, almost every manufactured product requires that holes are to be drilled for the purpose of assembly, creation of fluid passages, and so on. It is therefore desirable to monitor drill wear and hole quality changes during the hole drilling process. One important aspect in controlling the drilling process is drill wear status monitoring. With the monitoring, we may decide on optimal timing for tool change. The necessity of the detection of tool wear, fracture and the abnormal tool state has been emphasized in the machining process. Accordingly, this paper deals with the cutting characteristics of the hot-rolled high strength steels using common HSS drill. The performance variables include drill wear data obtained from drilling experiments conducted on the workpiece. The results are obtained from monitoring of the cutting force and Acoustic Emission (AE) signals, and from the detection of the abnormal tool state with the computer vision system.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.54 no.5
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• pp.198-205
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• 2005

Many precedent investigations hate been made for the reliable assessment of the insulation state of large power apparatus for which partial discharge detection is one of tile plausible way. In this work, experimental investigations have been carried out to make the comparison on the PD(partial discharge) pattern analysis related to the five different types of artificial defects such as SFMP (Single Free Moving Particle), MFMP (Multi Free Moving Particle), Void, CFP (Conductor-Fixed Protrusion), EP (Enclosure Protrusion). For each PD pattern, PD detection has been done by tee different types of PD sensors such as HFCT(High Frequency Current Transformer), AE(Acoustic Emission) and UHF(Ultra High Frequency). And, in addition, frequency spectrum by the UHF sensor has been also made for each defect respectively. As a result, it is observed that the possibility of obtaining PD pattern based on PRPD(Phase Resolved Partial Discharge) in connection with the defects tinder investigation is dependant on the type of the sensor while the spectrum analysis is always successful to be achieved for every defect. Therefore, it could be suggested that the nature of PD source can be identified more distinctively when the conventional PRPDA is combined with spectrum analysis.

• The Journal of Information Technology
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• v.2 no.2
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• pp.13-22
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• 1999

In this paper, we purpose automatic diagnosis in online, as the fundamental study to diagnose the partial discharge mechanism and to predict the lifetime by introduction a neural network. In the proposed method, we use AE(acoustic emission) sensing system and calculate a quantitative statistic parameter by pulse number and amplitude. Using statically parameters such as the center of gravity(G) and the gradient if the discharge distribute(C), we analyzed the early stage and the middle stage. the quantitative statistic parameters are learned by a neural network. The diagnosis of insulation degradation and a lifetime prediction by the early stage time are achieved. On the basis of revealed excellent diagnosis ability through the neural network learning for the patterns during degradation, it was proved that the neural network is appropriate for degradation diagnosis and lifetime prediction in partial discharge.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.993-998
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• 1997

The advanced technology of micro-machining is starting to penetrate our lives. This technology, with which it is possible to make micro-structures by means of processing on the order of nm (micrometer = 1/1,000 mm) or less, is realizing machines that were only part of our wildest imagination. However, the fact is that many issues remain in the quest for a variety of applications. With the advent of computing technologies, information technologies, and telecommunications technologies, we foresee the need for new approaches in design, process, and the use of materials, technologies, and people in a globalized manufacturing enterprise. A new thinking paradigm is needed to focus on quality of service on the products we design and manufacture. Factories in different regions need to be co-ordinated through use of the state-of-the-art information on productivity, diagnostics, and service evaluation of manufacturing systems could be shared among different locations and partners. In this research, We develope the internet based Diagnosis system for micro machining and evaluate its characteristics by using mechatronic sensor like Dynamometer, acoustic emission, Acceleration sensor, micro phone, vision, infra-red thermometer.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.25 no.3
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• pp.211-216
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• 2016

Al alloys are useful materials having high specific strength and are used in machining of parts having thin-walled structures for weight reduction in aircraft, automobiles, and portable devices. In machining of thin-walled structures, it is difficult to maintain dimensional accuracy because machining deformation occurs because of cutting forces and heat in the cutting zone. Thus, cutting conditions and methods need to be investigated and cutting signals need to be analyzed to diagnose and minimize machining deformation and thereby enhance machining quality. In this study, an investigation on cutting conditions to minimize machining deformation and an analysis on characteristics of cutting signals when machining deformation occurs are conducted. Cutting signals for the process are acquired by using an accelerometer and acoustic emission (AE) sensor. Signal characteristics according to the cutting conditions and the relation between machining deformation and cutting signals are analyzed.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.5
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• pp.90-95
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• 2007

Single side final polishing is a very important role to stabilize a wafer finally before the device process on the wafer is executed. In this study, the machining variables, such as pressure, machining time, and the velocity of pad table were adopted. These parameters have the major influence on the characteristics of wafer polishing. We investigated the surface roughness changing these variables to find the optimal polishing condition. Pad, slurry, slurry quantity, and oscillation distance were set to the fixed variables. In order to reduce defects and find a stable machining condition, a hall sensor was used on the polishing process. AE sensor was attached to the polishing machine to verify optimal condition. Applying data analysis of the sensor signal, experiments were performed. We can get better surface roughness from loading the quasi static force and improving wafer-holding method.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2000.10a
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• pp.101-108
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• 2000

In this paper, the real-time prediction of high temperature creep life was carried out for the friction welded joints of dissimilar heat resisting steels(SUH3-SUH35). Various life prediction methods such as LMP(Larson-Miller Parameter) and ISM(initial strain method) were applied : The creep behaviors of those steels and the welds under static load were examined by ISM combined with LMP at 500, 600 and $700^{\circ}C$, and the relationship between these two methods was investigated. A real-time creep life( $t_{r}$ , hr) prediction equation by initial strain($\varepsilon$$_{0}$ , %) under any creep stress ($\sigma$, MPa) at any high temperature(T, K) was developed as follows : $t_{r}$ =$\alpha$$\varepsilon$$_{0}$ $^{\beta}$$\sigma$$^{1}$ where, (equation omitted) for SUH3-SUH35 friction weld of =16mm and =20mm, respectively.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.205-208
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• 2006

Similar friction welding were produced using 15 mm diameter solid bar in Ni-base superalloy(alloy718) to investigate their mechanical properties. The main friction welding parameters were selected to endure good quality welds on the basis of visual examination, tensile tests, AE total counts and ultrasonic attenuation coefficient. The specimens were tested as welded, not heat-treated. The tensile strength of the friction welded joints was increased up to 90% of the alloy718 base metal under the condition of all heating time. Optimal welding conditions were n=2,000 (rpm), $P_1=200$ (MFa), $P_2=200$ (MFa), $t_1=8$ (s), $t_2=5$ (s) when the total upset length is 4.4(mm). The weld interface of similar friction welded steel bars was mixed strongly.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.388-393
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• 2012

Vibration condition monitoring at low rotational speeds is still a challenge. Acoustic emission (AE) is the most used technique when dealing with low speed bearings. At low rotational speeds, the energy induced from surface contact between raceway and rolling elements is very weak and sometimes buried by interference frequencies. This kind of issue is difficult to solve using vibration monitoring. Therefore some researchers utilize artificial damage on inner race or outer race to simplify the case. This paper presents vibration signal analysis of low speed slewing bearings running at a low rotational speed of 15 rpm. The natural damage data from industrial practice is used. The fault frequencies of bearings are difficult to identify using a power spectrum. Therefore the relatively improved method of empirical mode decomposition (EMD), ensemble EMD (EEMD) is employed. The result is can detect the fault frequencies when the FFT fail to do it.

• 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.