• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2001.10a
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• pp.134-137
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• 2001

In this paper on-line model is derived from investigating via series of finite element process simulation. Some variables that little affect on non-dimensional parameters. ie. forward slip and torque factor. is extracted from composing on-line model Especially, this research focused on deriving on-line model which exactly predict roll force and roll power in the roughing mill process under small shape factor and small reduction ratio. The prediction accuracy of the proposed model is examined through comparison with predictions from a finite element process model

• 한국신재생에너지학회:학술대회논문집
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• 2008.05a
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• pp.368-371
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• 2008

The optimal operation condition of gasifier is one of the most important parameters to increase efficiency and reliability in IGCC plant. Also the prediction of the syngas composition and quantity must be predicted to carry out process design of the gasification plant. However, the gasifier process licensor are protective with information on process design and optimal gasifier design conditions. So, the most of process studies in the engineering company for gasification plant have carried out to look for key parameters and optimal design conditions using several prediction methods. In this paper, we present the estimated preliminary optimal operation condition of the 300MW Demonstration Entrain Flow Gasifier using Aspen Plus. The gasifier operation temperature considering slag flow was predicted by FactSage software and Annen Model.

• Journal of Welding and Joining
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• v.16 no.6
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• pp.97-103
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• 1998

The bracket tilt among automobile parts is weld parts which construct the column assembly bracket tilt of equipments and accurate dimension after welding is more essential than weldment strength. By the way, it is insufficient that systematic study about this parts which have an importance on welding deformation. The reason is that welding deformation is complex problem with shape, size, material of parts and welding sequence, conditions etc. For reduction and removal of welding deformation, therefore, it is necessary that the security of welding deformation data and systematic examination about equipment, costs, work environment, manufacturing process etc. It is all the better that the prediction of welding deformation using simulation of welding process by FEA is supplemented. In this study, the countermeasure for this welding deformation of bracket tilt is brought up through experimental inspection before the choice of the optimum welding conditions with minimum welding deformation by simulation of welding process.

• Journal of Welding and Joining
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• v.16 no.6
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• pp.104-112
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• 1998

In the previous study, the countermeasure for welding deformation of bracket tilt is through up through experimental inspection for total process including welding process. For completeness of systematic examination of parts having sensitivity on welding deformation, the comparison and feedback between the result through simulation of welding process and experimental data is needed. In other words, it is necessary to control welding deformation that construct the prediction system for welding deformation through comparison and tuning with experimental data. In the present study, the application of FEA on welding process of bracket tilt with susceptibility to deformation is made and deformation behavior through change of welding sequence is focused on. It is used to improve the exactness of deformation analysis that three dimensional analysis for moving heat source, activated and deactivated bead element, and volume heat flux etc.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.3
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• pp.44-50
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• 2004

The purpose of this study is to improve the prediction ability of the atomization and vaporization processes of GDI spray under high-pressure and high-temperature conditions. Several models have been introduced and compared. The atomization process was modeled using hybrid breakup model that is composed of Conical Sheet Disintegration (CSD) model and Aerodynamically Progressed TAB(APTAB) model. The vaporization process was modeled using Spalding model, modified Spalding model and Abramzon ＆ Sirignano model. Exciplex fluorescence method was used for comparing the calculated with the experimental results. The experiment and calculation were performed at the ambient pressure of 0.5 MPa and 1.0 MPa and the ambient temperature of 473k. Comparison of caldulated and experimental spray characteristics was carried out and Abramzon ＆ Sirignano model and modified Spalding model had the better prediction ability for vaporization process than Spalding model.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1083-1086
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• 1996

Cold rolling mill process in steel works uses stands of rolls to flatten a strip to a desired thickness. At cold rolling mill process, precalculation determines the mill settings before a strip actually enters the mill and is done by an outdated mathematical model. A corrective neural network model is proposed to improve the accuracy of the roll force prediction. Additional variables to be fed to the network include the chemical composition of the coil, its coiling temperature and the aggregated amount of processed strips of each roll. The network was trained using a standard backpropagation with 4,944 process data collected from no.1 cold rolling mill process from March 1995 through December 1995, then was tested on the unseen 1,586 data from Jan 1996 through April 1996. The combined model reduced the prediction error by 32.8% on average.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.1000-1003
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• 2001

This paper presents the numerical analysis and experimental verification to know the metal cutting burr formation mechanism in face milling operation. Finite element method are applied to predict the 2-D burr formation process prediction. Face milling process are adjusted to analyze the characteristics of burr shapes according to various cutting conditions. The cutting parameters were investigated with cutting speed, feed rate, depth of cut. Through the experiments various burr types are classified according to its shape and properties.

• Journal of the Semiconductor & Display Technology
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• v.11 no.1
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• pp.49-54
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• 2012

There have been plenty of difficulties because properties of Alq3 are unable to acquire in a process of manufacture of OLED. In this paper it will predict a viscosity of Alq3 through DSMC technique and suggest the way regarding a study to estimate properties of material through the computer simulation. There could generate errors of a simulation process in a vacuum deposition process since the properties of material that is used in a high-degree vacuum environment are not secured. Therefore, we would like to propose the new methods that can not only predict properties of a molecular unit but also raise an accuracy of simulation process by forecasting properties of Alq3.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.4
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• pp.14-22
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• 2022

To reduce the weight of the car and ensure the safety of the driver while driving, the existing 440 MPa-class mounting bracket was treated at 590MPa to improve collision safety and secure the weight of the vehicle body. The following conclusions were drawn from the tensile test, forming analysis, and springback prediction. In the formability and springback analyses using FLD, it could be confirmed that bending was an essential process because the formability and flatness were much better when bending was added than when bending was not applied. Based on the research results, it was deduced that the mold design was necessary so that the molding was carried out at a strain rate of 20% or less for stable molding.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.841-854
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• 2020

The paper explains the method to process, analyze and predict traffic patterns in Los Angeles county using Big Data and Machine Learning. The dataset is used from a popular navigating platform in the USA, which tracks information on the road using connected users' devices and also collects reports shared by the users through the app. The dataset mainly consists of information about traffic jams and traffic incidents reported by users, such as road closure, hazards, accidents. The major contribution of this paper is to give a clear view of how the large-scale road traffic data can be stored and processed using the Big Data system - Hadoop and its ecosystem (Hive). In addition, analysis is explained with the help of visuals using Business Intelligence and prediction with classification machine learning model on the sampled traffic data is presented using Azure ML. The process of modeling, as well as results, are interpreted using metrics: accuracy, precision and recall.