Abstract
The rolling force model is the basis for reasonable selection of rolling equipment and optimization of rolling process, and the establishment of an accurate mathematical model as well as doing the corresponding parameter analysis has been the focus of research in this field for many years. Different modeling methods of the rolling force and their research progress were introduced, the main methods of which are the theoretical analysis, the finite element simulation, the artificial neural network modeling, the hybrid modeling of theory and neural network, as well as the hybrid modeling of finite element and neural network. Meanwhile, the application examples of rolling force models in thickness control, strip crown control, and schedule optimization were presented, and an outlook on the new directions of future development was made, including establishing new type of hybrid models, solving the black box problem, and realizing the multi-objective optimization accounting for some special requirements.
Similar content being viewed by others
References
X.C. Zeng, Research on precision and surface quality of NC incremental sheet metal forming, Zhejiang University of Science and Technology, Hangzhou, China, 2022.
T.V. Karman, Z. Angew. Math. Mech. 5 (1925) 139–141.
E. Orowan, P. I. Mech. Eng. A 150 (1943) 140–167.
R.B. Sims, P. I. Mech. Eng. A 168 (1954) 191–200.
J.J. Park, S.I. Oh, J. Eng. Ind. 112 (1990) 36–46.
N. Kim, S. Kobayashi, T. Altan, Int. J. Mach. Tools Manuf. 31 (1991) 553–563.
S.H. Zhang, G.L. Zhang, J.S. Liu, C.S. Li, R.B. Mei, Finite Elem. Anal. Des. 46 (2010) 1146–1154.
C. Lv, G.D. Wang, X.H. Liu, Z.Y. Jiang, H.T. Zhu, J.G. Yuan, Q. Jie, Iron and Steel 33 (1998) No. 3, 33–35.
Y. Li, W.Z. Liu, Y.K. Sun, Iron and Steel 31 (1996) No. 1, 54–57.
F. Zhang, Y. Zhao, J. Shao, J. Control Sci. Eng. 2016 (2016) 1–9.
H.Y. Wang, J.G. Ding, X. Lu, D.H. Zhang, D.W. Zhao, Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci. 231 (2017) 599–615.
Y.G. Li, H.Z. Li, J.M. Yu, Tianjin Metallurgy (2000) No. 4, 32–34.
Y.F. Zhang, H.S. Di, X. Li, D.W. Zhao, D.H. Zhang, J. Plast. Eng. 27 (2020) 153–158.
H. Ford, J.M. Alexander, J. Inst. Met. 92 (1964) 397–404.
S.I. Oh, S. Kobayashi, Int. J. Mech. Sci. 17 (1975) 293–305.
C.H. Moon, Y. Lee, ISIJ Int. 48 (2008) 1409–1418.
J. Pan, W. Pachla, S. Rosenberry, B. Avitzur, J. Eng. Ind. 106 (1984) 150–160.
S.H. Zhang, X.D. Chen, J.X. Hou, D.W. Zhao, Meccanica 51 (2016) 1189–1199.
J. Sun, Y.M. Liu, Y.K. Hu, Q.L. Wang, D.H. Zhang, D.W. Zhao, Int. J. Mech. Sci. 108–109 (2016) 166–173.
W. Peng, D. Zhang, D. Zhao, Int. J. Adv. Manuf. Technol. 91 (2017) 2233–2243.
S.H. Zhang, B.N. Song, X.N. Wang, D.W. Zhao, Appl. Math. Model. 38 (2014) 3485–3494.
Y. Yea, Y. Ko, N. Kim, J. Lee, J. Mater. Process. Technol. 140 (2003) 478–486.
Y.K. Kim, W.J. Kwak, T.J. Shin, S.M. Hwang, ISIJ Int. 50 (2010) 1644–1652.
S. Xiong, J.M.C. Rodrigues, P.A.F. Martins, Finite Elem. Anal. Des. 32 (1999) 221–233.
S.M. Hwang, M.S. Joun, Int. J. Mech. Sci. 34 (1992) 971–984.
W.J. Kwak, Y.H. Kim, H.D. Park, J.H. Lee, S.M. Hwang, ISIJ Int. 40 (2000) 1013–1018.
X. Duan, T. Sheppard, Int. J. Mech. Sci. 44 (2002) 2155–2172.
L.Z. Liu, X.H. Liu, G.D. Wang, J. Northeast. Univ. 23 (2001) 34–36.
J.L. Zhang, Z.S. Cui, J. Cent. South Univ. Technol. 18 (2011) 16–22.
X.H. Liu, X. Shi, S.Q. Li, J.Y. Xu, G.D. Wang, J. Iron Steel Res. Int. 14 (2007) No. 5, 22–26.
R. Ma, Principle of artificial neural network, China Machine Press, Beijing, China, 2010.
X.H. Liu, Q.L. Zhao, Z.Y. Huang, Steel Rolling 34 (2017) No. 4, 1–5.
G. Huang, G.B. Huang, S. Song, K.Y. You, Neural Networks 61 (2015) 32–48.
T.L. Fu, S.D. Wang, G.D. Wang, S.C. Wu, J. Northeast. Univ. 29 (2008) 1438–1442.
Y. Mahmoodkhani, M.A. Wells, G. Song, Ironmak. Steelmak. 44 (2017) 281–286.
Z.Y. Guo, J.N. Sun, F.S. Du, J. South. Afr. Inst. Min. Metall. 116 (2016) 43–48.
N. Ch, H.K. Ch, K.R. Venakata Kiran, P.S. Dandamudi, Eng. Res. Express 3 (2021) 035046.
Z. Wang, D. Zhang, D. Gong, W. Peng, ISIJ Int. 59 (2019) 1604–1613.
M. Bagheripoor, H. Bisadi, Appl. Math. Model. 37 (2013) 4593–4607.
S.H. Zhang, L. Deng, L.Z. Che, J. Manuf. Process. 75 (2022) 100–109.
C.G. Cui, Computer and Modernization (2019) No. 8, 74.
J. Li, X. Wang, Q. Yang, Z. Guo, L. Song, X. Mao, Int. J. Adv. Manuf. Technol. 121 (2022) 4087–4098.
S.H. Zhang, X.R. Jiang, F.X. You, Y.X. Li, Journal of Netshape Forming Engineering 12 (2020) No. 2, 8–14.
Z. Wang, Research on Iron and Steel 45 (2017) No. 3, 23–26.
X.N. Sun, Study and application on artificial neural network based rolling, Yanshan University, Qinhuangdao, Hebei, 2009.
M.J. Yang, Machine Tool Hydraulics 46 (2018) No. 22, 27–29.
J.Y. Liu, X.X. Liu, B.T. Le, Complexity 2019 (2019) 3476521.
Q.N. Wang, L.B. Song, J.W. Zhao, H.Y. Wang, Int. J. Adv. Manuf. Technol. 125 (2022) 387–397.
J. Cao, T. Wang, Y. Cao, C.N. Song, B. Gao, B. Wang, Int. J. Adv. Manuf. Technol. 115 (2021) 319–328.
R. Hwang, H. Jo, K.S. Kim, H.J. Hwang, IEEE Access 8 (2020) 153123–153133.
X.M. Ji, L. Wang, K.W. Gao, J. Liu, J. Iron Steel Res. 32 (2020) 393–399.
J.G. Ding, F.L. Liu, H.Z. Du, X. Li, D.H. Zhang, Steel Rolling (2022) http://kns.cnki.net/kcms/detail/11.2466.TF.20221024.1252.002.html.
Y.F. Ji, L.B. Song, J. Sun, W. Peng, H.Y. Li, L.F. Ma, J. Cent. South Univ. 28 (2021) 2333–2344.
J.F. Deng, J. Sun, W. Peng, Y.H. Hu, D.H. Zhang, Applied Soft Computing 78 (2019) 119–131.
Z.H. Wang, Y.M. Liu, D.Y. Gong, D.H. Zhang, Steel Res. Int. 89 (2018) 1800003.
Z.T. Zhao, L.Y. Zhu, X.Y. Gao, L. Wang, Metallurgical Industry Automation 46 (2022) No. 3, 34–41+93.
J. Sun, J. Deng, W. Peng, D. Zhang, Int. J. Precis. Eng. Manuf. 22 (2021) 301–311.
X. Li, Improvement of multi-objective particle swarm optimization algorithm and its application in rolling schedule optimization, Yanshan University, Qinhuangdao, Hebei, 2019.
Z.Y. Hu, Z.H. Wei, X.M. Ma, H. Sun, J.M. Yang, ISA Trans. 102 (2020) 193–207.
Y. Wang, J. Wang, C. Yin, Q. Zhao, IEEE Access 8 (2020) 80417–80426.
Z.W. Yan, H.N. Bu, C.Z. Hu, B. Pang, H.Y. Lyu, Int. J. Adv. Manuf. Technol. 125 (2023) 2869–2884.
L. Chen, W. Sun, A. He, T. Yuan, J. Shi, Y. Qiang, Metals 12 (2022) 924.
S.W. Wu, G.M. Cao, X.G. Zhou, Z.Y. Liu, J. Northeast. Univ. 37 (2016) 1710–1715.
Y. Zhang, R.M. Lin, H. Zhang, Y. Peng, Compl. Intell. Syst. (2022) No. 9, 133–145.
W.G. Li, W. Yang, Y.T. Zhao, B.K. Yan, X.H. Liu, J. Cent. South Univ. 26 (2019) 2379–2392.
Acknowledgements
The authors would like to extend their thanks to the financial support from the National Natural Science Foundation of China (Grant Nos. 52074187, U1960105, and 52274388). Also, the authors thank for the open-ended fund from Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology (No. MADTOF2022B01). The valuable suggestions from reviewers are also gratefully acknowledged.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors have no relevant financial or nonfinancial interests to disclose.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhang, Sh., Zhang, Y., Li, Wg. et al. Research progress and intelligent trend of accurate modeling of rolling force in metal sheet. J. Iron Steel Res. Int. 30, 2111–2121 (2023). https://doi.org/10.1007/s42243-023-01067-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42243-023-01067-4