FE-based On-line Model for the Prediction of Roll Force and Roll Power in Hot Strip Rolling.

Kwak, Wonshik; Kim, Y. H.; Park, H. D.; Lee, J. H.; Hwang, S.M.

doi:10.2355/isijinternational.40.1013

Cited by 27 publications

(6 citation statements)

References 2 publications

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“…To improve the prediction ability, various methods have been studied by many researchers. One of the most popular methods was to develop a new advanced physical model for the rolling force prediction (Barnett and Jonas, 1997;Kirihata et al, 1998;Kwak et al, 2000), and another widely used method was to generate an effective learning model to compensate the deficiency of the physical model (Cho et al, 1997;Hagan and Menhaj, 1994;Lee and Lee, 2002;Lu, 1998;Nishino et al, 2000). In this paper, the second approach is discussed in detail for its application in a plate mill.…”

Section: Introductionmentioning

confidence: 98%

Application of on-line adaptable Neural Network for the rolling force set-up of a plate mill

Lee

Choi

2004

Engineering Applications of Artificial Intelligence

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Section: Introductionmentioning

confidence: 98%

Application of on-line adaptable Neural Network for the rolling force set-up of a plate mill

Lee

Choi

2004

Engineering Applications of Artificial Intelligence

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“…In order to improve the accuracy of rolling force model, Chen et al [4] and Li et al [5] analyzed the friction law between the plate and roller and established a new rolling force model. Kwak et al [6,7] studied the influence of arm factor on rolling force and deduced a new rolling force model. Muller et al [8] systematically considered the friction coefficients and different material parameters in the rolling process and then established a more accurate rolling force model.…”

Section: Introductionmentioning

confidence: 99%

Modelling of Deformation Resistance with Big Data and Its Application in the Prediction of Rolling Force of Thick Plate

Zhang

Zhi

Liu

2021

Mathematical Problems in Engineering

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The precision of traditional deformation resistance model is limited, which leads to the inaccuracy of the existing rolling force model. In this paper, the back propagation (BP) neural network model was established according to the industrial big data to accurately predict the deformation resistance. Then, a new rolling force model was established by using the BP neural network model. During the establishment of the neural network model, the data set of deformation resistance was established, which was calculated back from the actual rolling force data. Based on the data set after normalization, the BP neural network model of deformation resistance was established through the optimization of algorithm and network structure. It is shown that both the prediction accuracy of the neural network model on the training set and the test set are high, indicating that the generalization ability of the model is strong. The neural network model of the deformation resistance is compared with the theoretical one, and the maximum error is only 3.96%. Furthermore, by comparison with the traditional rolling force model, it is found that the prediction accuracy of the rolling force model imbedding with the present neural network model is improved obviously. The maximum error of the present rolling force model is just 3.86%. The research in this paper provides a new way to improve the prediction accuracy of rolling force model.

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“…There have been numerous attempts to improve the prediction ability by developing more effective physical models for the rolling force prediction [5][6][7]. Recently, effective learning modelling techniques were successfully developed to improve the accuracy and compensate the shortcomings of previous models [8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Comparison of empirical and neural network hot-rolling process models

Öznergiz

Özsoy

Delice

et al. 2008

Proceedings of the Institution of Mechanical Engineers, Part B:

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Steel manufacturers are under pressure to improve their productivity levels by optimizing their process parameters to create maximum efficiency and quality levels. One of the keys to achieve this goal is the automation of the steel making process. Automation using artificial intelligence techniques applied to the hot rolling process is a potentially important steel manufacturing technique. The mathematical modelling of the process has been recognized as a desirable approach for designing mill equipment and ensuring productivity and service quality. However, such an analysis is generally very complex and time-consuming. Thus, there is considerable interest in developing simple and effective techniques to obtain accurate rolling force, torque, and slab temperatures. In this paper, a neural network (NN) is used to model the roughing stage of a plate hot rolling process. The NN approach can predict steady-state values of the force, torque, and slab temperature. The proposed NN model is compared with the classical empirical models commonly used in industrial practice. Experimental data obtained from the Ereğli Iron and Steel Factory in Turkey was used in developing the models.

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FE-based On-line Model for the Prediction of Roll Force and Roll Power in Hot Strip Rolling.

Cited by 27 publications

References 2 publications

Application of on-line adaptable Neural Network for the rolling force set-up of a plate mill

Application of on-line adaptable Neural Network for the rolling force set-up of a plate mill

Modelling of Deformation Resistance with Big Data and Its Application in the Prediction of Rolling Force of Thick Plate

Comparison of empirical and neural network hot-rolling process models

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