Reduced-Order Model Based on Neural Network of Roll Bending
Effective real-time control systems require fast and accurate models. The roll bending models presented in this paper are proposed for a real-time control system for the design of the rolling schedule. The roll bending, with other factors, defines the shape of the roll surface, its convexity, and fi...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/15/8418 |
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| author | Dmytro Svyetlichnyy |
| author_facet | Dmytro Svyetlichnyy |
| author_sort | Dmytro Svyetlichnyy |
| collection | DOAJ |
| description | Effective real-time control systems require fast and accurate models. The roll bending models presented in this paper are proposed for a real-time control system for the design of the rolling schedule. The roll bending, with other factors, defines the shape of the roll surface, its convexity, and finally the shape of the final product of the flat rolling, its convexity, and its flatness. This paper presents accurate finite element (FE) models for a four-high mill. The models serve to obtain accurate solutions to the problem of roll bending, taking into account the rolling force, width of the rolling sheet (strip), initial shape of the roll surface, and the anti-bending force. The results of the FE simulation are used to train three models developed on the basis of the neural network (NN) for the solution of one direct and two inverse tasks. The pre-trained NN model gives accurate results and is faster than the FE model (FEM). The calculation time on a personal computer for one case of 3D FEM is 1 to 2 min, for 2D FEM it is 1 s, and for NN it is less than 1 ms. The results can be immediately used by other models of the real-time control system. A novelty of the research presented in the paper is the creation of complex applications of the FE method and an NN as a reduced-order model (ROM) for prediction of roll bending and calculation of sheet (strip) convexity, rolling, and anti-bending forces to obtain the required convexity. |
| format | Article |
| id | doaj-art-07abde1689fe42feaaa8b3b6e6ae2476 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-07abde1689fe42feaaa8b3b6e6ae24762025-08-20T03:36:35ZengMDPI AGApplied Sciences2076-34172025-07-011515841810.3390/app15158418Reduced-Order Model Based on Neural Network of Roll BendingDmytro Svyetlichnyy0AGH University of Krakow, Faculty of Metals Engineering and Industrial Computer Science, al. Mickiewicza 30, 30-059 Krakow, PolandEffective real-time control systems require fast and accurate models. The roll bending models presented in this paper are proposed for a real-time control system for the design of the rolling schedule. The roll bending, with other factors, defines the shape of the roll surface, its convexity, and finally the shape of the final product of the flat rolling, its convexity, and its flatness. This paper presents accurate finite element (FE) models for a four-high mill. The models serve to obtain accurate solutions to the problem of roll bending, taking into account the rolling force, width of the rolling sheet (strip), initial shape of the roll surface, and the anti-bending force. The results of the FE simulation are used to train three models developed on the basis of the neural network (NN) for the solution of one direct and two inverse tasks. The pre-trained NN model gives accurate results and is faster than the FE model (FEM). The calculation time on a personal computer for one case of 3D FEM is 1 to 2 min, for 2D FEM it is 1 s, and for NN it is less than 1 ms. The results can be immediately used by other models of the real-time control system. A novelty of the research presented in the paper is the creation of complex applications of the FE method and an NN as a reduced-order model (ROM) for prediction of roll bending and calculation of sheet (strip) convexity, rolling, and anti-bending forces to obtain the required convexity.https://www.mdpi.com/2076-3417/15/15/8418reduced order modelneural networkroll bendingrollingreal-time control |
| spellingShingle | Dmytro Svyetlichnyy Reduced-Order Model Based on Neural Network of Roll Bending Applied Sciences reduced order model neural network roll bending rolling real-time control |
| title | Reduced-Order Model Based on Neural Network of Roll Bending |
| title_full | Reduced-Order Model Based on Neural Network of Roll Bending |
| title_fullStr | Reduced-Order Model Based on Neural Network of Roll Bending |
| title_full_unstemmed | Reduced-Order Model Based on Neural Network of Roll Bending |
| title_short | Reduced-Order Model Based on Neural Network of Roll Bending |
| title_sort | reduced order model based on neural network of roll bending |
| topic | reduced order model neural network roll bending rolling real-time control |
| url | https://www.mdpi.com/2076-3417/15/15/8418 |
| work_keys_str_mv | AT dmytrosvyetlichnyy reducedordermodelbasedonneuralnetworkofrollbending |