• 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.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.8 no.4
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• pp.129-135
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• 2009

In this paper, a finite element model is presented for the prediction of roll force and strip thickness in a backup-roll-drive mill. The proposed FE model is focused mainly on analyzing the elastic/plastic behavior between a work roll and a strip as well as the rigid/plastic behavior between a backup roll and a work roll. The capability of the proposed model is demonstrated through application to 4-high silicon steel rolling mill at POSCO. Results show that the predicted roll force and strip thickness rolled accurately agree with the measured them. It is also illustrated that the proper position of work roll displaced to one side from the vertical centerline of the backup-roll may be determined by minimizing the horizontal force of work 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.513-514
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• 2013

• 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.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2001.10a
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• pp.121-124
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• 2001

On-line prediction model which calculate roll force, roll power and forward slip of continuous hot strip rolling was built based on the results of plane strait rigid-viscoplastic finite element process model. Using the integrated FE process model, a series of finite element simulation was conducted over the process variables, and the influence of various process conditions on non-dimensional parameters was inspected. The prediction accuracy of the proposed on-line model under front and back tension is examined through comparison with predictions from a finite element process model over the various process conditions. In addition, we examined the validity of the on-line prediction model through comparison with roll force of experiment in hot rolling.

• Transactions of Materials Processing
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• v.19 no.6
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• pp.344-351
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• 2010

This paper presents a precision on-line model for the prediction of the roll force and tension distributions across the strip in hot strip rolling. The approach is based on an approximate 3-D theory of rolling, and in particular, considers the effect of pre-deformation of the strip, which occurs near the roll entrance before the strip enters the bite zone. The prediction accuracy of the proposed model is examined through comparison with the predictions from the 3-D finite element models.

• 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.

• Journal of Mechanical Science and Technology
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• v.16 no.4
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• pp.501-511
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• 2002

This paper presents a study of the effect of rolling temperature, roll gap (pass height), initial specimen size and steel grades of specimens on the roll force in round-oval-round pass sequence by applying approximate method and verifications through single stand pilot rod rolling tests. The results show that the predicted roll forces are in good agreement with the experimentally measured ones. The approximate model is independent of the change of roll gap, specimen size and temperature. Thus, the generality of the prediction methodology employed in the approximate model is proven. This study also demonstrates that Shida's constitutive equation employed in the approximate model needs to be corrected somehow to be applicable for the medium and high carbon steels in a lower temperature interval (700∼900$\^{C}$).

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1323-1326
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• 2003

In this paper, we propose set-up model of silicon steel cold rolling mill. Until now, the working of Silicon Steel is operated using the look-up table value of roll force which a field operator finds by making good use of his experience. Therefore, the standardization of data and an improvement of the quality on product are very difficult. So we establish neural model using field data of various kinds of coil at each pass.