• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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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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• v.5 no.5
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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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• v.9 no.2
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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

• Journal of Korean Society for Quality Management
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• v.30 no.2
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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.

• Korean Journal of Computational Design and Engineering
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• v.14 no.1
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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.

• Journal of Korea Multimedia Society
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• v.17 no.7
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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.

• The Journal of Society for e-Business Studies
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• v.13 no.3
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• pp.51-66
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• 2008

The Business Process Management System(BPMS) has received more attentions as companies increasingly realize the importance of business processes. However, traditional BPMS has focused mainly on correct modeling and exact automation of process flow, and paid little attention to the achievement of final goals of improving process efficiency and innovating processes. BPMS usually generates enormous amounts of log data during and after execution of processes, where numerous meaningful rules and patterns are hidden. In the present study we employ the data mining technique to find out useful knowledge from the complicated process log data. A data model and a system framework for process mining are provided to help understand the existing BPMS. Experiments with the simulated data demonstrate the effectiveness of the model and the framework.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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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.

• Journal of Korea Foundry Society
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• v.42 no.1
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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.