Profile Contour and Flatness Control Model for Hot Strip Rolling

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A full Profile Contour and Flatness Control (PCFC) model prototype has been developed for hot finishing mills. This model prototype accounts for several physical based sub-models calculating the different contributions to the roll gap profile and allows for offline predictions in both preset and recalculation modes. To evaluate the PCFC model developed, an exhaustive comparison analysis between its calculations, the ones coming from the plant model and measures at the finishing mill exit has been carried out. An industrial mill database composed of different rolling campaign types was applied for this purpose and both (i) strip crown and flatness indicators as well as (ii) full strip profiles results have been used for the comparisons. Encouraging results were obtained from this performance assessment since the PFCF model developed leads to similar behavior compared to the existing plant’s model (from an industrial supplier). As a result, the PCFC model developed shows high potential for online implementation in hot strip mills.

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Materials Science Forum (Volume 1186)

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85-96

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April 2026

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[1] Sato M, Kuchi M. Profile and Flatness Set Up System for Rolling Mill. IHI Engineering Review 2009;42:26–31.

Google Scholar

[2] K.N. Shohet, N.A. Townsend. Roll Bending Methods of Crown Control in Four-high Plate Mills. Journal of Iron and Steel Institute 1968:1088–98.

Google Scholar

[3] Bald W, Beisemann G, Feldmann H, Schultes T. Continuously variable crown (CVC) rolling. Iron and Steel Engineer 1987;64:32–41.

Google Scholar

[4] Wang X, Li F, Li B, Dong L, Zhang B. Design and Application of an Optimum Backup Roll Contour Configured with CVC Work Roll in Hot Strip Mill. ISIJ International 2012;52:1637–43.

DOI: 10.2355/isijinternational.52.1637

Google Scholar

[5] Seilinger, A., Mayrhofer, A., Kainz, A. SmartCrown - a new system for improved profile and flatness control in rolling mills. Rev Met Paris 2003;100:43–8.

DOI: 10.1051/metal/2003001

Google Scholar

[6] Guo R-M. Characteristics of Rolling Mills with Roll Shifting. Iron and Steel Technology 1988;65:45–54.

Google Scholar

[7] Zhao J, Wang X, Yang Q, Wang Q, Liu C, Song G. High precision shape model and presetting strategy for strip hot rolling. Journal of Materials Processing Technology 2019;265:99–111.

DOI: 10.1016/j.jmatprotec.2018.10.005

Google Scholar

[8] Li H, Xu J, Wang G, Shi L, Xiao Y. Development of strip flatness and crown control model for hot strip mills. Journal of Iron and Steel Research International 2010;17:21–7.

DOI: 10.1016/S1006-706X(10)60067-2

Google Scholar

[9] D. Ehlert, O. Jepsen, G. Schneider. OPTIMIZATION OF HIGH QUALITY PRODUCTION IN HOT ROLLING MILLS USING ADVANCED PROCESS MODELS. 52nd Rolling Seminar, Rio de Janeiro 2015:99–108.

DOI: 10.5151/1983-4764-26275

Google Scholar

[10] Peng K, Zhong H, Zhao L, Xue K, Ji Y. Strip shape modeling and its setup strategy in hot strip mill process. The International Journal of Advanced Manufacturing Technology 2014;72:589–605.

DOI: 10.1007/s00170-014-5649-2

Google Scholar

[11] Auzinger D, Gjumlija G, Nijhuis T, Seilinger A, Widder M, Posch G, et al. Application of advanced technology packages for improved strip profile and flatness in hot-strip-mills. Rev Met Paris 2003;100:977–86.

DOI: 10.1051/metal:2003111

Google Scholar

[12] Deng J, Sierla S, Sun J, Vyatkin V. Physics-informed generative regression for industrial process modeling in steel strip rolling. Expert Systems with Applications 2025;282:127713.

DOI: 10.1016/j.eswa.2025.127713

Google Scholar

[13] Wang Z, Huang Y, Liu Y, Wang T. Prediction Model of Strip Crown in Hot Rolling Process Based on Machine Learning and Industrial Data. Metals 2023;13:900.

DOI: 10.3390/met13050900

Google Scholar

[14] Liu Y, Wang X, Sun J, Liu G, Li H, Ji Y. Strip Thickness and Profile–Flatness Prediction in Tandem Hot Rolling Process Using Mechanism Model-Guided Machine Learning. Steel Research International 2023;94:2200447.

DOI: 10.1002/srin.202200447

Google Scholar

[15] Song C, Cao J, Sun L, Tan X, Xia W, Sun S. A multi-stand work roll bending and shifting approach for profile contour and flatness control of electrical steel in multi-width schedule-free rolling using NSGA-II algorithm. Journal of Manufacturing Processes 2024;120:895–910.

DOI: 10.1016/j.jmapro.2024.05.016

Google Scholar

[16] Deng J, Sun J, Peng W, Hu Y, Zhang D. Application of neural networks for predicting hot-rolled strip crown. Applied Soft Computing 2019;78:119–31.

DOI: 10.1016/j.asoc.2019.02.030

Google Scholar

[17] Meng LM, Ding JG, Dong ZS, Li X, Zhang DH. Crown Prediction of Hot-Rolled Silicon Steel Using Transfer Learning Network Fused with Whale Optimization Algorithm. Steel Research International 2023;94:2300105.

DOI: 10.1002/srin.202300105

Google Scholar

[18] Ji Y, Wen Y, Peng W, Sun J. Predicting Hot-rolled Strip Crown Using a Hybrid Machine Learning Model. ISIJ International 2024;64:566–75.

DOI: 10.2355/isijinternational.ISIJINT-2023-203

Google Scholar

[19] Song L, Xu D, Wang X, Yang Q, Ji Y. Application of machine learning to predict and diagnose for hot-rolled strip crown. The International Journal of Advanced Manufacturing Technology 2022;120:881–90.

DOI: 10.1007/s00170-022-08825-w

Google Scholar

[20] Song C, Cao J, Xiao J, Zhao Q, Sun S, Li Y. Control strategy of multi-stand work roll bending and shifting on the crown for UVC hot rolling mill based on MOGPR approach. Journal of Manufacturing Processes 2023;85:832–43.

DOI: 10.1016/j.jmapro.2022.11.075

Google Scholar

[21] Shang F, Chen H, Wang S, Zhao J, Ma Y, Ba Y. Machine learning-based process parameter optimisation and strip plate shape improvement in steel production. Ironmaking & Steelmaking 2025;52:975–1010.

DOI: 10.1177/03019233251359803

Google Scholar

[22] Hacquin A, Montmitonnet P, Guillerault JP. A three-dimensional semi-analytical model of rolling stand deformation with finite element validation. European Journal of Mechanics - A/Solids 1998;17:79–106.

DOI: 10.1016/S0997-7538(98)80065-X

Google Scholar

[23] A. Hacquin. Modélisation thermomécanique tridimensionnelle du laminage: Couplage bande-cylindres (in french). PhD. Thesis. Ecole des Mines de Paris, 1996.

Google Scholar

[24] K. Nakajima, T. Asamura, T. Kikuma, H. Matsumoto. Hot strip crown control by 6-high mills. Iron and Steel Institute of Japan 1984;24.

DOI: 10.2355/isijinternational1966.24.284

Google Scholar

[25] S. Timoshenko, J. N. Goodier. Theory of Elasticity (pp.406-410). McGraw-Hill book; 1951.

Google Scholar

[26] N. Souto, E. Marchand, A. Gay, Z. Koont, N. Legrand. Performance Analysis of Work-Roll Wear Models on Hot Rolling. Key Engineering Materials 2022;926:621–31.

DOI: 10.4028/p-q6v323

Google Scholar

[27] Archard JF, Hirst W. The Wear of Metals under Unlubricated Conditions. Proceedings of the Royal Society of London Series A, Mathematical and Physical Sciences 1956;236:397–410.

DOI: 10.1098/rspa.1956.0144

Google Scholar

[28] N. Legrand, N. Souto, S. Abdelkhalek, Z. Koont. Tec3 Work-Roll Thermal Crown Model for Hot and Cold Rolling of steel. 11th International Rolling Conference (IRC 2019) 2019:955–66.

Google Scholar