• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.987-990
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• 2000

In modern manufacturing industry such as aerospace, vehicle and die/mold industry, the high hardness malarial which is remarkable in aspects of durability is effectively used. The high-speed and precision machining technology has been applied in these fields. In this study, efficient sensors in high-speed machining by observing similar tendency through comparing cutting force with AE signal, gap sensor signal and accelerometer signal are selected, and machinability of high-speed machining is experimentally evaluated. We performed a basic research for sensing system construction to monitor a machine tool and machining condition.

• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.17-18
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• 2010

최근 이슈가 되고 있는 클라우드 컴퓨팅은 다수의 노드를 이용한 클러스터를 사용한다. 이러한 클러스터를 효율적으로 관리하기 위해 모니터링 툴을 사용하고 있다. 하지만, 기존의 모니터링 툴은 클러스터를 구성하는 노드의 가용성과 오버헤드, 데이터 수집/전송 방식에 중심을 둔 모니터링 툴이기 때문에 클라우드 클러스터의 세부 정보까지 모니터링 할 수 없다. 따라서 본 논문에서는 클라우드 컴퓨팅을 구축할 수 있는 플랫폼인 Hadoop을 위한 모니터링 툴을 제안한다.

• Convergence Security Journal
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• v.20 no.5
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• pp.27-32
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• 2020

The Windows Event Log is a Log that defines the overall behavior of the system, and these files contain data that can detect various user behaviors and signs of anomalies. However, since the Event Log is generated for each action, it takes a considerable amount of time to analyze the log. Therefore, in this study, we designed and implemented an XML-based Event Log analysis tool based on the main Event Log list of "Spotting the Adversary with Windows Event Log Monitoring" presented at the NSA.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.10
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• pp.50-56
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• 1995

Chatter Vibration is an unwanted phenomenon in metal cutting and it always affects surface finish, tool life, machine life and the productivity of machining process. The real-time detection of the chatter vibration is is necessarily required to automation system. In this study, we constructed the multi-sensing system using Tool Dynamometer, Accelermeter and AE sensor. Especially, Acoustic Emission(AE) generated during turning was investigated the possibility for real-time detection of chatter vibration. Turning experiments were performed using carbide insert tip under realistic cutting conditions and tapered workpiece of SM45C. Consquently, the real-time detection using multi-sensing system can be used for Inprocess monitoring of chatter vibration.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.6
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• pp.867-875
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• 2004

In order to inspect burr geometry and hole quality in micro-drilling processes, a cost-effective method using an image processing and shape from focus (SFF) methods on the machine tool is proposed. A CCD camera with a zoom lens and a novel illumination unit is used in this paper. Since the on-machine vision unit is incorporated with the CNC function of the machine tool, direct measurement and condition monitoring of micro-drilling processes are conducted between drilling processes on the machine tool. Stainless steel and hardened tool steel are used as specimens, as well as twist drills made of carbide are used in experiments. Validity of the developed system is confirmed through experiments.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1570-1575
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• 1991

The feasibility of using an adaptive resonance network (ART2) with unsupervised learning capability for too] wear detection in turning operations is investigated. Specifically, acoustic emission (AE) and cutting force signals were measured during machining, the multichannel AR coefficients of the two signals were calculated and then presented to the network to make a decision on tool wear. If the presented features are significantly different from previously learned patterns associated with a fresh tool, the network will recognize the difference and form a new category m worn tool. The experimental results show that tool wear can be effectively detected with or without minimum prior training using the self-organization property of the ART2 network.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.8
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• pp.1939-1951
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• 1994

This study introduces a new tool breakage detecting technology comprised of an unsupervised neural network combined with adaptive time series autoregressive(AR) model where parameters are estimated recursively at each sampling instant using a parameter adaptation algorithm based on an RLS(Recursive Least Square). Experiment indicates that AR parameters are good features for tool breakage, therefore it can be detected by tracking the evolution of the AR parameters during milling process. an ART 2(Adaptive Resonance Theory 2) neural network is used for clustering of tool states using these parameters and the network is capable of self organizing without supervised learning. This system operates successfully under the wide range of cutting conditions without a priori knowledge of the process, with fast monitoring time.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.5 no.3
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• pp.15-21
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• 1996

This study is focused on the prediction of appropriate tool life by clarifying the correlation between progressive tool wear and AE(Acoustic Emission) signals, while cutting stainless steel by end mill on the machining center. The results of this study were that RMSAE tends to increase linearly along with the increase of the cutting speed, and it was more sensitive to depth of cut than to the variation of feed rate at the same cutting conditions, and RMSAE increases around 0.21mm flank wear hereby AE-HIT also increases. AE signals depend upon tool wear and fracture from the above results. Therefore, the AE signals can be utilized in order to monitor the tool condition.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.5 no.3
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• pp.73-79
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• 1996

In this paper present a new technique (strain-telemetering)for detection of coated tool failure in face milling processes. In the cutter body the strain signals received fro the transmitter is transformed in to frequency modulation(FM) signals in face milling processes. A receiver which is place near by the Vertical milling machine receives the FM signals, then the signals will be sent to a computer which determines whether th tool is failure. And machined surface of workpiece is detected by the SEM. In this paper, A on-line monitoring of the tool failure detection system based on the strain -telemetering apparatus has bee represented.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.5
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• pp.714-719
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• 2012

In this paper, the polishing force monitoring and the control method were implemented for the polishing machine with the airbag tool. Airbag tool has been known to be adaptable to the curvature variation such as the aspherical and the free-form surface. However, it was necessary to control the tool movement of vertical axis also because of the table rotational wobble and vibration. To solve it by the polishing force control, we installed another stepping motor to the z-axis. And the polishing force was measured with the load cell and controlled by the PID Labview controller. A few hundreds gram of the polishing force were well controlled under 0.8 second of the response time and 5% variation. An experiment was done to clean the edge burrs of the micro channel structure of width $87{\mu}m$ using the polishing force control.