• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2008.10a
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• pp.211-214
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• 2008

The Mechanical properties of steel in hot strip mill were associated with the various rolling conditions such as alloy composition, plastic deformation, cooling history and so on. After coiling process of strip which is the end of hot rolling process, the coil can be the final product or can be applied by another process, that is, cold rolling or skin pass rolling with the additional changes of mechanical properties. Skin pass rolling process with the small reduction affects the mechanical properties of the strip. Because many kinds of hot strips are delivered to the customers after the skin pass process, it is important for us to know the skin pass effects for the mechanical properties of the hot rolling strip. In this study, the variations of mechanical properties of the strip after the skin pass rolling will be discussed. Then, the mathematical model will be proposed for the prediction of mechanical properties of the final products with the comparison between measured and calculated values.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.9
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• pp.869-874
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• 2015

We developed an LSDC (Load Shift and Load Distribution Control) technology in order to improve coil quality and productivity by reducing tension fluctuation especially for the tail of the strip in the down coiler in hot strip mills. To adapt the new controller, the torque and speed distribution between the zero pinch roll, pinch roll, and mandrel are needed. The proposed controller is a combination of an LSC to share the tension between the mill stand and the mandrel, and an LDC to shift the torque load from the zero pinch roll to the pinch roll. From the simulation, the proposed controller is verified under the torque disturbance. Using a field test, the torque deviation decreased by nearly 50% through utilization of the LSDC control.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.4
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• pp.382-387
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• 2011

In this paper, a LBCC (Latter Bank Cooling Control) for the high strength steel is proposed to obtain the desirable temperature and the property of the material along the longitudinal direction of the steel on the ROT (Run-Out Table) process. A cooling valve is modeled to analyze the response of the ROT banks. The control concept is derived from a field data, a valve model considering the valve response and a TTT (Time-Temperature Transformation) diagram. The proposed control is verified from the simulation results under the various carbon quantities. It is shown through the field test of the hot strip mill that the deviation of the CT (Coiling Temperature) is considerably decreased by the proposed temperature control.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.7
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• pp.2257-2265
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• 1996

In the cold rolling mill, it is very important that a constrained static flow stress of rolled strip and rolling force calculation model be exactly considered to improve an prediction accuracy for rolling forces. Therefore, in this study, the values of the constrained static flow stress are used by deriving the regression equation which is a function of rolling conditions(FDT, CT) and chemical compositions(C, Si, Mn), previously applied by making the tables of yield strength for hot coils with size. And with the consideration that an elastic deformation part of an rolled strip appears at the entry and delivery side of the contacting area between the work roll and rolled strip is calculated. By applying these methods, the more accurate prediction for rolling force is obtained. As a results, the deviation of thickness is significantly reduced in the rolling direction.

• 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 Operations and Management Science Society Conference
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• 1996.04a
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• pp.63-66
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• 1996

Cold rolling mill process in steel works uses stands of rolls to flatten a strip to a desired thichness. At Pohang Iron and Steel Company (POSCO) in Pohang, Korea, 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 2,277 process data collected form POSCO from March 1995, then was tested on the unseen 200 data from the same period. The combined model reduced the prediction error by 55.4% on average.

• IE interfaces
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• v.9 no.3
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• pp.298-305
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• 1996

Cold rolling mill process in steel works uses stands of rolls to flatten a strip to a desired thickness. Most of rolling processes use mathematical models to predict rolling force which is very important to decide the resultant thickness of a coil. In general, these mathematical models are not flexible for variant coil types and cannot handle various elements which is practically important to decide accurate rolling force. A corrective neural network is proposed to improve the accuracy of rolling force prediction. Additional variables-composition of the coil, coiling temperature and working roll parameters-are fed to the network. The model uses an MLP with BP to predict a corrective coefficient. The test results using 1,586 process data collected at POSCO in early 1995 show that the proposed model reduced the prediction error by 30% on average.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.8
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• pp.39-47
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• 1999

This paper describes on-line retrainable RBF network in order to control the coiling temperature for a hot coil strip at Pohang Iron & Steel Company(POSCO). The proposed neural network can be used for improving conventional rule-based lookup table, which generates a heat transmission coefficient. To cope with time-varying characteristics of hot coil process, additional synaptic weights for on-line retraining purposes are introduced to hidden-to-output weights of conventional RBF network. Those weights are locally adjusted to newly incoming test data while preserving old information trained with off-line past data. Hence the effect of catastrophic interference can be greatly alleviated with the proposed network. In addition, rejection scheme is introduced for reliability concerns. From the experimental results applied to the actual process, it is noticed that overall control performance represents about 2.2% increase compared to the conventional one.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.1
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• pp.29-35
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• 2015

In this study, a FEM analysis is undertaken of a rubber sleeve which is mounted onto a spreading mandrel so as to avoid marking the first wrappings of coils (known as the 'end-mark'), as occasionally occurs when a concentrated load is placed on the edge of a steel sheet, significantly reducing its quality. A commercial numerical package, ANSYS, was utilized to analyze the structural behavior of the rubber sleeve. In general, the strain of the sleeve increases as the thickness of the rubber layer (H) covering the tubes increases, thus also increasing the surface of the sleeve for a constant boundary condition, and decreasing the pitch (P) between each tube, resulting in an increase in the strain on the surface of the sleeve for all rubber thickness conditions tested here. In a comparison of two different materials, rubber and urethane, when H=3 mm and P=1.1D, the maximum total deformations in these cases are 0.12669 mm and 0.086623 mm, respectively.