• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1997.10a
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• pp.115-118
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• 1997

Recently, hot rolling oil lubrication technology is required to face with the new environments such as the rapid introduction of high wear resistent high speed steel roll the development of continuous hot rolling technology. In the hot strip mill, according to rolling and quality required conditions are constrict, Roll material of hot rolling finishing stand is changing Hi-Cr Roll to High Speed Steel [HSS] Roll. The problem of HSS Roll of roll force and strip scale defects are increasing in hot strip mill, So we have tested HSS Roll in hot rolling simulator as rolling condition, rolling speed, draft, hot oil concentration. To reduce roll force and prevent scale defects. We get some merit rolling force, rolling torque, roll wear reduction, roll and strip surface roughness and hot rolling critical oil concentration 0.4%. Finally we are going to investigate the effect of hot rolling oil of on rolling with HSS Roll.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.368-373
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• 2003

A major concern at present is the simultaneous control of transverse thickness profile and flatness in the finishing stages of hot rolling process. The mathematical modeling of hot rolling process has long been recognized to be a desirable approach to investigate rolling operating practice and the design of mill equipment to improve productivity and quality. However, many factors make the mathematical analysis of the rolling process very complex and time-consuming. In order to overcome these problems and to obtain an accurate rolling force, the predicted model of rolling force using neural networks has widely been employed. In this paper, Radial Basis Function Network(RBFN) is applied to improve the accuracy of rolling force prediction in hot rolling mill. In order to verify and analysis the performance of applied neural network, the comparison with the measured rolling force and the predicted results using two different neural networks - RBFN, MLP, has respectively been carried out. The results obtained using RBFN neural network are much more accurate those obtained the MLP.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.1
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• pp.52-57
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• 2005

In the foe of global competition, the requirements for the continuously increasing productivity, flexibility and quality(dimensional accuracy, mechanical properties and surface properties) have imposed a mai or change on steel manufacturing industries. Indeed, one of the keys to achieve this goal is the automation of the steel-making process using AI(Artificial Intelligence) techniques. The automation of hot rolling process requires the developments of several mathematical models for simulation and quantitative description of the industrial operations involved. In this paper, an on-line training neural network for both long-term teaming and short-term teaming was developed in order to improve the prediction of rolling force in hot rolling mill. This analysis shows that the predicted rolling force is very closed to the actual rolling force, and the thickness error of the strip is considerably reduced.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.124-129
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• 2003

In the face of global competitor the requirements flor the continuously increasing productivity, flexibility and quality(dimensional accuracy, mechanical properties and surface properties) have imposed a major change on steel manufacturing industries. Indeed, one of the keys to achieve this goal is the automation of the steel-making process using AI(Artificial Intelligence) techniques. The automation of hot rolling process requires the developments of several mathematical models fir simulation and quantitative description of the industrial operations involved. In this paper, a on-line training neural network for both long-term teaming and short-term teaming was developed in order to improve the prediction of rolling force in hot rolling mill. This analysis shows that the predicted rolling force is very closed to the actual rolling force, and the thickness error of the strip is considerably reduced.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.6
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• pp.29-33
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• 2004

A major concern at present is the simultaneous control of transverse thickness profile and flatness in the finishing stages of hot rolling process. The mathematical modeling of hot rolling process has long been recognized to be a desirable approach to investigate rolling operating practice and the design of mill equipment to improve productivity and quality. However, many factors make the mathematical analysis of the rolling process very complex and time-consuming. In order to overcome these problems and to obtain an accurate rolling force, the predicted model of rolling force using neural networks has widely been employed. In this paper, Radial Basis Function Network(RBFN) is applied to improve the accuracy of rolling force prediction in hot rolling mill. In order to verify and analyze the performance of applied neural network the comparison with the measured rolling force and the predicted results using two different neural networks-RBFN, MLP, has respectively been carried out. The results obtained using RBFN neural network are much more accurate those obtained the MLP.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.2
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• pp.271-277
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• 2013

Skin pass rolling is a very important process for applying a certain elongation to a strip in the cold rolling and annealing processes, which play an important role in preventing the stretching of the yield point when the material is processed. The exact prediction of the rolling force is essential for obtaining a given elongation with the steel grade and strip size. Unlike hot rolling and cold rolling, skin pass rolling is used to apply an elongation of within 2% to the strip. Under a small reduction, it is difficult to predict the rolling force because the elastic deformation behavior of the rolls is complicated and a model for predicting the rolling force has not yet been established. Nevertheless, the exact prediction of the rolling force in skin pass rolling has gained increasing importance in recent times with the rapid development of high-strength steels for use in automobiles. In this study, the possibility of predicting the rolling force in skin pass rolling for producing various steel grades was examined using foil rolling theory, which is known to have similar elastic deformation behavior of rolls in the roll bite. It was found that a noncircular arc model is more accurate than a circular model in predicting the roll force of high-strength steel below TS 980 MPa in skin pass rolling.

• Journal of Mechanical Science and Technology
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• v.16 no.10
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• pp.1296-1302
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• 2002

A mathematical model for cold rolling and temper rolling process of thin steel strip has been developed using the influence function method. By solving the equations describing roll gap phenomena in a unique procedure and considering more influence factors, the model offers significant improvements in accuracy, robustness and generality of the solution for the thin strip cold and temper rolling conditions. The relationship between the shape of the roll profile and the roll force is also discussed. Calculation results show that any change increasing the roll force may result in or enlarge the central flat region in the deformation zone. Applied to the temper rolling process, the model can well predict not only the rolling load but also the large forward slip. Therefore, the measured forward slip, together with the measured roll force, was used to calibrate the model. The model was installed in tile setup computer of a temper rolling mill to make parallel setup calculations. The calculation results show good agreement with the measured data and the validity and precision of the model are proven.

• Structural Engineering and Mechanics
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• v.47 no.4
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• pp.455-470
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• 2013

This paper presents a theory concerning the beam element subjected to an eccentric rolling disk (or simply called the eccentric-disk-loaded beam element) such that the dynamic responses of a beam subjected to an eccentric rolling disk with its inertia force, Coriolis force and centrifugal force considered can be easily determined. To this end, the property matrices of an eccentric-disk-loaded beam element are firstly derived by means of the Lagrange's equations. Then, the overall property matrices of the entire vibrating system are determined by directly adding the property matrices of the eccentric-disk-loaded beam element to the overall ones of the entire beam itself. Finally, the Newmark direct integration method is used to solve the equations of motion for the dynamic responses of a beam subjected to an eccentric rolling disk. Some factors relating to the title problem, such as the eccentricity, radius and rotating speed of the rolling disk, and the Coriolis force and centrifugal force induced by the rolling disk are investigated. Numerical results reveal that the influence of last factors on the dynamic responses of the pinned-pinned beam is significant except the centrifugal force.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.723-727
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• 2003

In this paper, two methods are discussed for good rolling force prediction in a plate mill. One is about the development of a long and a short learning scheme using a Neural Network for normal rolling and the other is about a mathematical model improvement by considering microstructural changes for controlled rolling. The research results are implemented in a on-line system of Pohang Works in POSCO and the field tests have showed that the prediction accuracies of rolling force are highly improved.

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.3
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• pp.116-126
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• 2010

The Korean rolling stock safety regulation stipulates that the collision deceleration of a car body should be maintained under average 5g and maximum 7.5g during train collisions. One-dimensional dynamic model of a full rake train, which is made up of nonlinear springs/bars-dampers-masses, is often used to estimate the collision decelerations of car bodies in a basic design stage. By the way, the previous studies have often used some average force-deformation curve for energy absorbing structures in rolling stock. Through this study, we intended to analyse how much the collision deceleration levels are influenced by the initial peak force modeling in the one-dimensional force-deformation curve. The numerical results of the one-dimensional dynamic model for the Korean High-Speed Train show that the initial peak force modeling gives significant effect on the collision deceleration levels. Therefore the peak force modeling of the force-deformation curve should be considered in one-dimensional dynamic model of a full rake train to evaluate the article 16 of the domestic rolling stock safety regulations.