• 동력기계공학회지
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• 제4권2호
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• pp.25-30
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• 2000

The grinding process is very complex and relates many parameters to control the process. As this reason, a theoretical analysis and a quantitative estimation of the grinding process has not been well established. In this study, the in-process monitoring system was suggested by applying the neural network for monitoring and shooting the malfunction of cylindrical plunge grinding process. This system used the power signals from the electric power meter. This neural network was composed of processing elements [4-(5-5)-3] with 4 identified power parameters. Because sensitivity is blunted some minute vibration components, the simulation result of this system has appeared about 10% erroneous recognition in the uncertain pattern and the average success rate of the trouble recognition was about 90%. Consequently, the developed system, which applied to the power signals, can be recognize enough to monitor the grinding process as in-process.

• 한국산학기술학회논문지
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• 제11권6호
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• pp.2256-2262
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• 2010

비즈니스 프로세스의 자동화된 처리와 이에 대한 모니터링 요구는 기업마다 특화된 형태를 지니는 것이 일반적이다. 비즈니스 프로세스는 일반적인 데이터베이스 트랜잭션과는 달리 수행에 장시간이 소요되며, 비즈니스 상황에 따라 매우 복잡한 처리 로직을 가진다. 이러한 처리 로직들은 사업 정책이나 환경이 변함에 따라 자주 변경되기 때문에 기업은 특정 비즈니스 프로세스의 모니터링 과정에서 해당 프로세스 인스턴스의 전체적 의미를 종합적으로 파악하고자 한다. 본 연구에서는 비즈니스 프로세스의 종합적인 모니터링을 위해 GMA(Grid Monitoring Architecture)를 사용하였다. GMA는 이종 시스템 환경 하에서 모니터링 정보를 효과적으로 관리하고 감독하기 위하여 제시된 확장성이 높은 아키텍처이다. 다양한 처리 로직을 지원할 수 있도록 GMA 모델에 복합 이벤트의 처리 기능을 부가함으로써, 비즈니스 프로세스에 대한 자동화된 실행과 상위수준의 모니터링이 가능한 시스템을 구축할 수 있었다.

• 한국레이저가공학회지
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• 제6권2호
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• pp.27-33
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• 2003

A laser shock cleaning technology is a new dry cleaning methodology for the effective removal of small particles from the surface. This technique uses a plasma shock wave produced by a breakdown of air due to an intense laser pulse. In order to optimize the laser shock cleaning process, it needs to evaluate the cleaning performance quantitatively by using a monitoring technique. In this paper, an acoustic monitoring technique was attempted to investigate the laser shock cleaning process with an aim to optimize the cleaning process. A wide-band microphone with high sensitivity was utilized to detect acoustic signals during the cleaning process. It was found that the intensity of the shock wave was strongly dependent on the power density of laser beam and the gas species at the laser beam focus. As a power density was larger, the shock wave became stronger. It was also seen that the shock wave became stronger in the case of Ar gas compared with air and N$_2$ gas.

• 한국산학기술학회논문지
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• 제8권5호
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• pp.1082-1086
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• 2007

본 연구는 국내에서 발생되는 소각재를 재활용하여 벽돌 생산을 하는 공장에서의 이원화된 작업현황을 일원화 할 수 있는 데이터 모니터링 시스템 구축을 목적으로 한다. 모니터링 시스템은 상태관리 프로세스, 위치관리 프로세스, 불량관리 프로세스, 상황관리 프로세스 등의 데이터 관리 프로세스를 이용해 데이터를 자동 관리하도록 설계하였다. 본 연구에서는 RFID 태그를 이용하여 각 공정의 상황 정보를 수집하고 수집된 정보를 가공하여 데이터 모니터링 시스템에 전송한다. 이러한 시스템을 통해 분석된 데이터는 소각재를 재활용하여 벽돌 생산을 하는 공장에서 공정자동화, 불량률 최소화, 실시간 모니터링, 적재 관리를 통해 생산 공정을 효율적으로 관리할 수 있다.

• 전기학회논문지P
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• 제56권1호
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• pp.13-18
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• 2007

In this paper, authors deal with an application of power quality monitoring using the Sampled Value which is described in the IEC61850 International Standard for substation communication. Firstly, while Merging Unit is designed as a process level device transmitting sensor data, the practical problems such as time delay compensation and optical fiber communication are issued. Secondly, the Sampled Value message which is proper to a power quality monitoring system is presented. Because the power quality monitoring system requests non time critical service comparing to protection and control applications, the Sampled Value service message structure is introduced to improve efficiency. At last, the power quality monitoring server having various power quality analysis functions is suggested to verify the performance of Merging Unit. With the diverse experiments, it is proved that the process bus distributed solution is flexible and economic for the power quality monitoring.

• 한국정밀공학회지
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• 제13권9호
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• pp.149-155
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• 1996

Recent researches in the trouble monitoring system of grinding process have emphasized the use of deep knowledge. Such works include the monitoring and diagnostic systems for cylindrical grinding using sensors on chatter vibration and grinding burn during the process. But, since grinding operations are especially related with a lalrge amount of ambique parameters, it is effectively difficult to detect the grinding troubles occuring during the grinding process. In this paper, monitoring system for grinding utilizes the neural networks based on grinding power signatures. The monitoring system of grinding operations, which makes use of PDP neural networks, is presented. Then, the implementation results by computer simulations and experimental data with respect to chatter vibration and grinding burn are compared.

• Journal of the Korean Data and Information Science Society
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• 제18권3호
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• pp.773-782
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• 2007

The statistical process control charts are very extensively used for monitoring of process mean, deviation, defect rate or reject rate. In this paper we consider a control chart to monitor the process reject rate in the high yield process, which is based on the observed cumulative probability of the number of items inspected until r defective items are observed. We first propose selection of the optimal value of r in the CPC-r charts, and also consider the usefulness of the chart in high yield process such as semiconductor or TFT-LCD manufacturing process.

• 약학회지
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• 제60권2호
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• pp.85-91
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• 2016

Drying of granules for tablet formulation is one of the important unit operations. The loss on drying method is traditionally used for this purpose. However, it is a time-consuming method, requiring at least 1 h. Moreover, it is ineffective in monitoring the moisture content of granules during the drying process. In this study, online real-time monitoring of moisture content during the drying process was successfully performed using near-infrared (NIR) spectroscopy. NIR spectra were collected during 15 different drying batches for developing a reliable NIR spectroscopic method. Such a large number of batches were used to develop a more robust partial least squares (PLS) model. NIR spectra collected from 12 batches were used for developing the model that was validated by predicting the moisture content of the samples in the remaining 3 batches. The standard errors of predictions (SEPs) in the measurement of batch 1, batch 2, and batch 3 were 0.52%, 0.57%, and 0.56%, respectively. The online NIR spectroscopic method developed in this study was reliable and accurate in monitoring the moisture content during the drying process.

• 한국기계가공학회지
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• 제21권6호
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• pp.89-97
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• 2022

Ultrasonic metal welding has been widely used for joining lithium-ion battery tabs. Weld quality monitoring has been an important issue in lithium-ion battery manufacturing. This study focuses on the weld quality monitoring in ultrasonic metal welding with the longitudinal-torsional vibration mode horn developed newly. As the quality of ultrasonic welding depends on welding parameters like pressure, time, and amplitude, the suitable values of these parameters were selected for experimentation. The welds were tested via tensile testing machine and weld strengths were investigated. The dataset collected for performance test was used to train the multi-layer perceptron neural network. The three layer neural network was used for the study and the optimum number of neurons in the first and second hidden layers were selected based on performances of each models. The best models were selected for the horn and then tested to see their performances on an unseen dataset. The neural network models for the longitudinal-torsional mode horn attained test accuracy of 90%. This result implies that proposed models has potential for the weld quality monitoring.

• 인터넷정보학회논문지
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• 제21권3호
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• pp.83-92
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• 2020

최근에 많은 기업 및 조직들이 비즈니스 프로세스 모델의 효율적 운용을 위해 예측적 프로세스 모니터링에 관심이 높아지고 있다. 기존의 프로세스 모니터링은 특정 프로세스 인스턴스의 경과된 실행상태에 초점을 두었다. 반면, 예측적 프로세스 모니터링은 특정 프로세스 인스턴스의 미래의 실행상태에 대한 예측에 초점을 둔다. 본 논문에서는 예측적 프로세스 모니터링 기능 중 하나인 비즈니스 프로세스 인스턴스 실행 잔여시간 예측기능을 구현한다. 잔여시간을 효과적으로 모델링하기 위해 액티비티별 속성에 따른 시간특징 값 분포 차이를 고려하여 액티비티별 특징 정규화를 제안하고 예측모델에 적용한다. 본 논문에서 제안된 모델의 예측성능 우수성을 입증하기 위해서 4TU.Centre for Research Data에서 제공하는 실제 기업의 이벤트 로그 데이터를 통해 선행연구들과 비교평가 한다.