• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.281-282
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• 2010

• Transactions of Materials Processing
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• v.28 no.4
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• pp.197-202
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• 2019

This paper proposes a precise mathematical model for the prediction of the variation of the roll force across a strip in cold rolling. It further describes the deformation characteristics of the strip using a 3-D finite element method. The different features of hot rolling and cold rolling through a 3-D finite element method are shown. The predicted roll force profile and tension profile are verified through comparison with the prediction from a 3-D finite element method.

• Transactions of Materials Processing
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• v.28 no.4
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• pp.190-196
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• 2019

The capability of accurately predicting the roll force profile across a strip in the bite zone in cold rolling process is vital for the calculation of strip profile. This paper presents a derivation of a precision mathematical model for predicting variations in the roll force across a strip in cold rolling. While the derivation is based on an approximate 3-D theory of rolling, this mathematical model also considers plastic deformation in the pre-deformation region which is located close to the roll entrance before the strip enters the bite zone. Finally, the mathematical model is expressed as a boundary value problem, and it predicts the roll force profile and tension profile in addition to lateral plastic strain profile.

• 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.

• 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.

• 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.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1379-1382
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• 2007

In this paper, we present a simplified analytic approach for the prediction of roll force to be applicable to the grooveless rolling. The approach is based on the deformation shape deduced from physical considerations and employs the assumption that the deformation homogeneously occurs in three directions. Strain and strain rate are calculated by the geometric relationships between those components and the prescribed deformation functions. Then, stress components are obtained from the Levy-Mises' flow rule. By integrating the stress components along the rolling direction, roll force are finally obtained. The prediction accuracy of the proposed model is examined through comparison with results obtained from the finite element analysis.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2004.08a
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• pp.175-183
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• 2004

In hot strip rolling, a capability for precisely predicting roll force is crucial for sound process control. In the past, on-line prediction models have been developed mostly on the basis of Orowan's theory and its variation. However, the range of process conditions in which desired prediction accuracy could be achieved was rather limited, mainly due to many simplifying assumptions inherent to Orowan's theory. As far as the prediction accuracy is concerned, a rigorously formulated finite element(FE) process model is perhaps the best choice. However, a FE process model in general requires a large CPU time, rendering itself inadequate for on-line purpose. In this report, we present a FE-based on-line prediction model applicable to precision process control in a finishing mill(FM). Described was an integrated FE process model capable of revealing the detailed aspects of the thermo-mechanical behavior of the roll-strip system. Using the FE process model, a series of process simulation was conducted to investigate the effect of diverse process variables on some selected non-dimensional parameters characterizing the thermo-mechanical behavior of the strip. Then, it was shown that an on-line model for the prediction of roll force could be derived on the basis of these parameters. The prediction accuracy of the proposed model was examined through comparison with measurements from the hot strip mill.

• Transactions of Materials Processing
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• v.20 no.8
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• pp.548-554
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• 2011

A Finite Element (FE)-based model is proposed for predicting the roll force in an edger. The model is developed on the basis of the hypothetical mode of rolling and the least-squares regression analysis from the result of the FE approach. This model reflects the effect of process variables affected by the roll force, and has three dimensionless parameters, I.e., shape factor, reduction ratio and width-to-thickness ratio. The model prediction compared satisfactorily with experiment observations.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.323-323
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• 2000

In the tandem cold wiling mill, the quality is very in portant, and requirements for thickness accuracy become more strict. However, the mathematical model for prediction of rolling force was not considered an elastic deformation at the entry and delivery side of the contacted area between the worked roll and rolling strip so that there was so difficult to control of the thickness. To overcome this problem, the mathematical model included an elastic deformation of strip has been developed and applied to the field in order to predict the rolling force. The simulated results showed that the end of elastic recovery should be included the model, even if the effect of elastic compression was not important.