• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 추계학술대회 논문집
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• pp.591-594
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• 2002

Generating NC-code from 3D part model needs a lot of effort to make many decisions, including machining area, tool change data, tool data, cutting condition, etc., by using either manual or computer aided method. This effort can be reduced by integration of automated process planning and NC-code generation. In case of generating NC code with a help of the process planning system, many data mentioned from the process planning can be used. It means that we can create NC-code about a full part. In this study, integration of FAPPS(Feature based Automatic Process Planning) with a NC-code generating module is described and additional data to adapt NC-code for machine shop is discussed.

• International Journal of Precision Engineering and Manufacturing-Green Technology
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• 제5권5호
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• pp.571-581
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• 2018

In the present study, monitoring of elliptical vibration cutting process by utilizing internal data in the ultrasonic elliptical vibration device without external sensors such as a dynamometer and displacement sensor is investigated. The internal data utilized here is the change of excitation frequency, i.e. resonant frequency of the device, voltages applied to the piezoelectric actuators composing the device, and electric currents flowing through the actuators. These internal data change automatically in the elliptical vibration control system in order to keep a constant elliptical vibration against the change of the cutting process. Correlativity between the process and the internal data is described by using a vibration model of ultrasonic elliptical vibration cutting and verified by several experiments, i.e. planing and mirror surface finishing of hardened die steel carried out with single crystalline diamond tools. As a result, it is proved that it is possible to estimate the elements of elliptical vibration cutting process, e.g. tool wear and machining load, which are important for stable cutting in such precision machining.

• Journal of the Korean Data and Information Science Society
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• 제9권2호
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• pp.305-310
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• 1998

In this paper, we introduce the conditions that the P-process has the intensity function which it is a standard form of gamma distribution. And we show that the transformed geometric Poisson process which the intensity function is a standard form of gamma distribution is a alternative sign P-process

• 품질경영학회지
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• 제30권2호
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• pp.72-85
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• 2002

Advances in computer and sensor technology have made it possible to obtain superlarge manufacturing process data in real time, letting us extract meaningful information from these superlarge data sets. We propose a systematic data analysis procedure which field engineers can apply easily to manufacture quality products. The procedure consists of data cleaning and data analysis stages. Data cleaning stage is to construct a database suitable for statistical analysis from the original superlarge manufacturing process data. In the data analysis stage, we suggest a graphical easy-to-implement approach to extract practical information from the cleaned database. This study will help manufacturing companies to achieve six sigma quality.

• 한국CDE학회논문집
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• 제14권1호
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• pp.1-9
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• 2009

ISO 15926 is an international standard for the representation of process plant lifecycle data. However, it is not easy to implement the part 2-data model and the part 4-initial reference data because of their complexity in terms of data structure and shortages of related development toolkits. To overcome this problem, ISO 15926-7(part 7) is under development. ISO 15926-7 specifies implementation methods for sharing and exchange of process plant lifecycle data, which is based on semantic web technologies such as OWL, Web Services, and SPARQL. For the application of ISO 15926-7, this paper discusses how to represent technical specifications of process plant equipment by defining user-defined reference data and object information model with an example of reactor coolant pumps located in the reactor coolant system of an APR 1400 nuclear power plant.

• 한국멀티미디어학회논문지
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• 제17권7호
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• pp.858-865
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• 2014

In the case of system like MES, various sensors collect the data in real time and save it as a big data to monitor the process. However, if there is big data mining in distributed computing system, whole processing process can be improved. In this paper, system to analyze the cause of operation deviation was built using the big data which has been collected from deasphalting process at the two different plants. By applying multivariate statistical analysis to the big data which has been collected through MES(Manufacturing Execution System), main cause of operation deviation was analyzed. We present the example of analyzing the operation deviation of deasphalting process using the big data which collected from MES by using multivariate statistics analysis method. As a result of regression analysis of the forward stepwise method, regression equation has been found which can explain 52% increase of performance compare to existing model. Through this suggested method, the existing petrochemical process can be replaced which is manual analysis method and has the risk of being subjective according to the tester. The new method can provide the objective analysis method based on numbers and statistic.

• 한국전자거래학회지
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• 제13권3호
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• pp.51-66
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• 2008

기업이 비즈니스 프로세스의 중요성을 인식하게 되면서 비즈니스 프로세스 관리시스템에 대한 관심이 높아졌다. 그러나 전통적으로 BPMS는 업무프로세스의 모형화 및 실행에 그 초점이 맞추어져 있어, 상대적으로 프로세스 관리의 궁극적인 목적인 프로세스 효율성 제고나 더 나아가 프로세스를 혁신하는 데에는 큰 기여를 하지 못하였다. BPMS는 일반적으로 방대한 량의 이력 데이터를 남기게 되는데, 이에는 프로세스의 실행과 관련된 다양한 의미있는 정보나 규칙들이 숨어있다. 본 연구는 데이터마이닝 기법을 활용하여 프로세스 이력 데이터를 분석하는 체계적인 방법론을 제시한다. 제시된 방법론은 프로세스 분석을 위한 데이터 모형, 분석절차, 그리고 시스템 프레임워크를 포함한다. 또한 그 유용성을 검증하기 위하여, 시뮬레이션 데이터를 이용한 실험결과를 제시한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.664-667
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• 1997

For higher component density per chip, it is necessary to identify and control the semiconductor manufacturing process more stringently. Recently, neural networks have been identified as one of the most promising techniques for modeling and control of complicated processes such as plasma etching process. Since wafer states after each run using identical recipe may differ from each other, conventional neural network models utilizing input factors only cannot represent the actual state of process and equipment. In this paper, in addition to the input factors of the recipe, real-time tool data are utilized for modeling of 64M DRAM s-poly plasma etching process to reflect the actual state of process and equipment. For real-time tool data, we collect optical emission spectroscopy (OES) data. Through principal component analysis (PCA), we extract principal components from entire OES data. And then these principal components are included to input parameters of neural network model. Finally neural network model is trained using feed forward error back propagation (FFEBP) algorithm. As a results, simulation results exhibit good wafer state prediction capability after plasma etching process.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2006년도 춘계학술대회 논문집
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• pp.615-616
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• 2006

Many manufacturing companies manage their production-related data for quality management and production management. Nevertheless, production related-data should be closely related to each other Stored data is mainly used to monitor their process and products' error. In this paper, we provide an enterprise-wide production data model for decision support system and product automation. Process data, quality-related data, and test data are integrated to identify the process inter or intra dependency, the yield forecasting, and the trend of process status. In addition, it helps the manufacturing decision support system to decide critical manufacturing problems.

• 한국주조공학회지
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• 제42권1호
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• pp.35-41
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• 2022

In the present paper, the data management of casting processes has been discussed. In order to construct a smart factory in the foundry industry, understanding of the whole casting processes has to be in the first place. Casting process data can be obtained at the kiosk operated by casting engineers and data acquired by sensors in the foundry facility. However, preprocessing of the casting process data must be carried out in order to analyze the casting process by the data. Techniques and some examples for data preprocessing in the foundry was introduced.