Investigation of the combined effect of friction and interstand tension on the work conditions of a two-stands reversing cold mill

Investigation of the combined effect of friction and interstand tension on the work conditions of a two-stands reversing cold mill

Antonio Piccininni, Mattia Antonicelli, Angela Cusanno, Pasquale Guglielmi, Donato Sorgente, Gianfranco Palumbo

Abstract. Cold-rolled strips are largely used in various fields of applications, such as electric works, automobiles, home appliances, and light industry. In addition to the growing demand for cold-rolled strips, customers are increasingly concerned about the final quality of the products. The factors that affect the performance of steel strips can be divided into two categories: material parameters and rolling conditions. The latter are generally unknown and, therefore, a systematic methodology to inversely determine the coefficients of friction is of utmost importance. Moreover, when dealing with more complex systems, as in the case of a two-stands rolling mill, the friction conditions are strictly connected to the interstand state of tension, and their calibration becomes even more difficult. This paper investigates the combined effect of the friction and the interstand tension during the cold rolling of the SAE1006 steel grade. Experimental data were taken directly on-site from the two-stands reversing cold mill (RCM). The rolling process was modelled using the commercial finite element (FE) code Abaqus/CAE: rolls were modelled as elastic bodies (to account for their flattening during the rolling operations) and the process parameters from RCM were simulated. A full factorial plan of simulations was arranged by varying the coefficient of friction in the first stand (indicated as CoF1) and the one in the second stand (indicated as CoF2): numerical results were collected in terms of rolling force and interstand stress and compared with the data obtained by on-site measurements. It was demonstrated that the proper values of CoF1 and CoF2 could be successfully determined (by minimizing the error function between numerical and experimental data) only when considering also their effect on the interstand state of stress.

Keywords
Cold Rolling, Friction, Finite Element Model, Inverse Analysis, Optimization

Published online 5/7/2025, 9 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Antonio Piccininni, Mattia Antonicelli, Angela Cusanno, Pasquale Guglielmi, Donato Sorgente, Gianfranco Palumbo, Investigation of the combined effect of friction and interstand tension on the work conditions of a two-stands reversing cold mill, Materials Research Proceedings, Vol. 54, pp 868-878, 2025

DOI: https://doi.org/10.21741/9781644903599-93

The article was published as article 93 of the book Material Forming

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

References
[1] J.T. Black, R.A. Kohser, E.P. DeGarmo, Materials and processes in manufacturing, wiley, 2003.
[2] P. Munther, J.G. Lenard, Tribology during hot, flat rolling of steels, CIRP Annals 44 (1995) 213–216.
[3] A.A. Radionov, O.I. Petukhova, I.N. Erdakov, A.S. Karandaev, B.M. Loginov, V.R. Khramshin, Developing an automated system to control the rolled product section for a wire rod mill with multi-roll passes, Journal of Manufacturing and Materials Processing 6 (2022) 88.
[4] G. Zheng, Z. Yang, R. Cao, W. Zhang, H. Li, Adaptive learning prediction on rolling force in the process of reversible cold rolling mill, in: Intelligent Robotics and Applications: 5th International Conference, ICIRA 2012, Montreal, QC, Canada, October 3-5, 2012, Proceedings, Part I 5, Springer, 2012: pp. 66–75.
[5] E. Orowan, The Calculation of Roll Pressure in Hot and Cold Flat Rolling, Proceedings of the Institution of Mechanical Engineers 150 (1943) 140–167. https://doi.org/10.1243/PIME_PROC_1943_150_025_02
[6] T. Von Karman, On the theory of rolling, Z. Angew. Math. Mech 5 (1925) 139–141.
[7] A. Lanzutti, J. Srnec Novak, F. De Bona, D. Bearzi, M. Magnan, L. Fedrizzi, Failure analysis of cemented carbide roller for cold rolling: Material characterisation, numerical analysis, and material modelling, Eng Fail Anal 116 (2020) 104755. https://doi.org/https://doi.org/10.1016/j.engfailanal.2020.104755
[8] H. Pawelski, Friction inhomogeneities in cold rolling, J Mater Process Technol 125–126 (2002) 392–397. https://doi.org/https://doi.org/10.1016/S0924-0136(02)00350-3
[9] N. Fujita, Y. Kimura, K. Kobayashi, Y. Amanuma, Y. Sodani, Estimation model of plate-out oil film in high-speed tandem cold rolling, J Mater Process Technol 219 (2015) 295–302. https://doi.org/https://doi.org/10.1016/j.jmatprotec.2015.01.002
[10] H. Hitchcock, Roll neck bearings, Appendix I, Elastic Deformation of Rolls during Cold Rolling (1935) 33–41.
[11] Z.Y. Jiang, A.K. Tieu, X.M. Zhang, C. Lu, W.H. Sun, Finite element simulation of cold rolling of thin strip, J Mater Process Technol 140 (2003) 542–547. https://doi.org/https://doi.org/10.1016/S0924-0136(03)00832-X
[12] R. Mehrabi, M. Salimi, S. Ziaei-Rad, Finite Element Analysis on Chattering in Cold Rolling and Comparison With Experimental Results, J Manuf Sci Eng 137 (2015). https://doi.org/10.1115/1.4030379
[13] L. Li, H. Xie, T. Liu, X. Li, X. Liu, M. Huo, E. Wang, J. Li, H. Liu, L. Sun, Z. Jiang, Effects of Rolling Force on Strip Shape during Tandem Cold Rolling Using a Novel Multistand Finite Element Model, Steel Res Int 93 (2022) 2100359. https://doi.org/https://doi.org/10.1002/srin.202100359
[14] L.J.M. Jacobs, K.N.H. Van Dam, D.J. Wentink, M.B. De Rooij, J. Van der Lugt, D.J. Schipper, J.P.M. Hoefnagels, Effect of asymmetric material entrance on lubrication in cold rolling, Tribol Int 175 (2022) 107810.
[15] K. Devarajan, K.P. Marimuthu, A. Ramesh, FEM analysis of effect of rolling parameters on cold rolling process, Bonfring International Journal of Industrial Engineering and Management Science 2 (2012) 35.
[16] U.S. Dixit, P.M. Dixit, Application of Fuzzy Set Theory in the Scheduling of a Tandem Cold-Rolling Mill, J Manuf Sci Eng 122 (1999) 494–500. https://doi.org/10.1115/1.1285866
[17] Y. Wang, Y. Li, J. Liu, An adaptive weight PSO for rolling schedules multi-objective optimization of tandem cold rolling, in: 2009 IEEE International Conference on Automation and Logistics, 2009: pp. 895–899. https://doi.org/10.1109/ICAL.2009.5262796
[18] W. Jiang, L. Liu, Y. Wang, B. Mao, Application of improved ant colony algorithm in load distribution optimization of tandem cold rolling, in: 2011 Second International Conference on Mechanic Automation and Control Engineering, IEEE, 2011: pp. 1070–1075.
[19] D.D. Wang, A.K. Tieu, G. D’Alessio, Computational Intelligence-Based Process Optimization for Tandem Cold Rolling, Materials and Manufacturing Processes 20 (2005) 479–496. https://doi.org/10.1081/AMP-200053535
[20] M. Poursina, N.T. Dehkordi, A. Fattahi, H. Mirmohammadi, Application of genetic algorithms to optimization of rolling schedules based on damage mechanics, Simul Model Pract Theory 22 (2012) 61–73. https://doi.org/https://doi.org/10.1016/j.simpat.2011.11.005
[21] D.D. Wang, A.K. Tieu, F.G. de Boer, B. Ma, W.Y.D. Yuen, Toward a heuristic optimum design of rolling schedules for tandem cold rolling mills, Eng Appl Artif Intell 13 (2000) 397–406. https://doi.org/https://doi.org/10.1016/S0952-1976(00)00016-6
[22] Y. Wang, C. Li, X. Jin, Y. Xiang, X. Li, Multi-objective optimization of rolling schedule for tandem cold strip rolling based on NSGA-II, J Manuf Process 60 (2020) 257–267. https://doi.org/https://doi.org/10.1016/j.jmapro.2020.10.061
[23] J.S. Xia, M. Khaje Khabaz, I. Patra, I. Khalid, J.R.N. Alvarez, A. Rahmanian, S.A. Eftekhari, D. Toghraie, Using feed-forward perceptron Artificial Neural Network (ANN) model to determine the rolling force, power and slip of the tandem cold rolling, ISA Trans 132 (2023) 353–363. https://doi.org/https://doi.org/10.1016/j.isatra.2022.06.009
[24] S. Poles, MOGA-II An improved Multi-Objective Genetic Algorithm, ModeFRONTIER User Manual (2003) 16.
[25] S. Poles, E. Rigoni, T. Robič, MOGA-II Performance on Noisy Optimization Problems, Proceedings of the International Conference on Bioinspired Optimization Methods and Their Applications (2004) 51–62.
[26] A. Piccininni, G. Palumbo, Inverse calibration of the friction conditions in cold rolling by means of on-site force monitoring, International Journal of Advanced Manufacturing Technology 128 (2023) 3599–3611. https://doi.org/10.1007/s00170-023-12118-1
[27] A. Piccininni, G. Palumbo, Inverse calibration of the friction conditions in cold rolling by means of on-site force monitoring, The International Journal of Advanced Manufacturing Technology 128 (2023) 3599–3611. https://doi.org/10.1007/s00170-023-12118-1