• 한국공작기계학회논문집
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• 제11권3호
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• pp.39-44
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• 2002

As laser cutting process is widely used in industry, an automated on-line process control system has become very important. In this paper, development of a laser cutting monitoring system, which is regarded as the fundamental step toward automation of the process, is presented. Acoustic emission and an artificial neural network were used for the monitoring system. With given process Parameters including laser power and scanning speed the system can predict the suitability of laser beam for the cutting or a stainless steel (STS304) plate.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 추계학술대회 논문집
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• pp.200-205
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• 1996

Recently, in order to achieve high flexibility of manufacture, monitoring and control strategies cf a new type have been developed. Since the generation of built-up edge on the cutting tool damages the surface finish of the workpiece, the monitoring system of built-up edge is an important process monitoring. In this study, the analyzing methods of cutting force signal to detect the built-up edge during cutting process are described. The cutting force signals are analyzed using the mean, standard deviation and mean to standard deviation of this cutting signals. We can obtain the guide to detect the built-up edge during turning process.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2014년도 추계학술대회 논문집
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• pp.739-742
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• 2014

In the process of MLCC manufacturing, MLCC stacking process is the key process of making high quality MLCC. Since MLCC is small components, the entire process of MLCC stacking process is minute and sensitive to micro errors. To prevent micro error, we suggest condition-based monitoring which quantifies error based on feature extraction and quantifying error method. As results, it has been shown that the suggested algorithm has effectiveness of condition based monitoring of MLCC stacker.

• Journal of Welding and Joining
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• 제28권4호
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• pp.81-86
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• 2010

In resistance spot welding, regardless of the optimal condition, bad weld quality was still produced due to complicated manufacturing processes such as electrode wear, misalignment between the electrode and workpiece, poor part fit-up, and etc.. Therefore, the goal of this study was to measure the process signal which contains weld quality information, and to develop the process fault monitoring system. Welding force signal obtained through variety experimental conditions was analyzed and divided into three categories: good, shunt, and poor fit-up group. And then a monitoring algorithm made up of an artificial neural network that could estimate the process fault of each different category based on pattern was developed.

• 한국정밀공학회지
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• 제14권7호
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• pp.67-72
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• 1997

Recently, in order to achieve high flexibility of manufacture, monitoring and control strategies of a new type have been developed. Since the generation of built-up edge on the cutting tool damages the surface finish of the workpiece, the monitoring system of built-up edge is an important process monitoring. In this study, the analyzing methods of cutting force signal to detect the built-up edge during cutting process are described. The cutting force signals are analyzed using the mean, standard deviation and mean to standard deviation of this cutting signals. We can obtain the guide to detect the built-up edge during turning process.

• 대한안전경영과학회지
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• 제2권4호
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• pp.19-32
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• 2000

Assignable causes producing temporary deviation from the underlying system can influence on process adjustment and process monitoring in dynamic feedback control system. In this paper, the impact of assignable causes on EWMA forecasts and process adjustment which is based on the EWMA forecasts are derived for optimum control methods.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.11-21
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• 1992

Modern manufacturing process requires machine intelligence to meet the demands for high technology products as well as intelligence-based operating skills to lessen human worker's intervene. To meet this trend there has been wide spread interest in applying artificial neural network(ANN) to the areas of manufacturing process monitoring and control. This paper addresses application problems in such processes as welding, assembly, hydroforming process and inspection of solder joints.

• 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.

• Journal of Biosystems Engineering
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• 제23권4호
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• pp.373-380
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• 1998

The contribution of this paper is to provide the methods for the production automation of potato microtuber using the vision process in growth monitoring. The first method deals with computation for the growth density in the primary growth process. The second method addresses cognition process to identify the number and the volume of potato microtuber in secondary growth process. The third is to decide whether potato microtubers are infected by a virus or bacteria in growth process. The computation for the growth density in the primary growth process uses the method of Labeling. The second and third methods use template matching based on color patterns. With the developed method using vision process, this experiment is capable of discriminating weekly growth-rate in primary growth process, 85% cognition rate in secondary process and identifying whether there are infections. Therefore, we conclude that our experimental results are capable of growth monitoring for mass production of potato microtubers.

• 한국컴퓨터정보학회논문지
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• 제18권3호
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• pp.107-118
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• 2013

본 논문에서는 반도체 식각 공정 모니터링 시스템을 개발한다. 반도체 산업은 첨단 산업 중, 전자제품의 필수 부품을 생산하는 대표적인 고부가가치 산업으로, 세계 각국에서 치열한 개발 경쟁을 벌이고 있다. 이에 따라 반도체 제품의 품질과 특성, 그리고 생산성을 향상하기 위한 많은 연구들이 진행되고 있는데, 공정 모니터링 기술이 이에 해당한다. 실제로 반도체 회로를 형성하는 식각 공정에서의 불량은 큰 피해를 야기 시키므로, 공정을 상세히 모니터링 할 수 있는 시스템의 개발이 필요하다. 본 논문에서 기술하는 반도체 식각 공정 모니터링 시스템은 플라즈마를 이용한 건식식각 공정을 상세하게 관찰 분석하여 관리자에게 피드백하고, 설정된 시나리오에 맞게 자동으로 공정을 제어하여 공정 자동화 효율을 극대화한다. 실시간으로 모니터링을 수행하고 그 결과를 즉각적으로 시스템에 반영한다. 관리자는 시스템에서 제공하는UI(User Interface)를 통해 공정의 현재 상태를 진단할 수 있다. 시스템은 관리자가 사전에 작성한 공정 시나리오를 따라 공정을 자동으로 제어하고, 공정중단 시점을 효율적으로 찾아내어 생산 효율을 높인다.