Iterative Convergence for Solving the Exit Plastic Zone and Friction Coefficient Model of Ultra-thin Strip Rolling Force

被引:1
作者
Zhang, Jie [1 ,2 ,3 ]
Wang, Tao [1 ,2 ,3 ]
Wang, Zhenhua [1 ,2 ,3 ]
Liu, Xiao [1 ,2 ,3 ]
机构
[1] Taiyuan Univ Technol, Coll Mech & Vehicle Engn, Yingze West St 79, Taiyuan 030024, Shanxi, Peoples R China
[2] Taiyuan Univ Technol, Engn Res Ctr Adv Met Composites Forming Technol &, Minist Educ, Yingze West St 79, Taiyuan 030024, Shanxi, Peoples R China
[3] Taiyuan Univ Technol, Natl Key Lab Met Forming Technol & Heavy Equipmen, Yingze West St 79, Taiyuan 030024, Shanxi, Peoples R China
基金
中国国家自然科学基金;
关键词
ultra-thin strip; Fleck theory; frictional coefficient model; GWO-KELM neural network; rolling force prediction model; DEFORMATION; DESIGN; FOIL;
D O I
10.2355/isijinternational.ISIJINT-2024-214
中图分类号
TF [冶金工业];
学科分类号
0806 ;
摘要
For the analytical model of rolling force of ultra-thin strip, the iterative conditions of the exit plastic zone are improved to solve the convergence problem of the Fleck model in small reduction rolling. The nonlinear law of friction coefficient in multi-pass rolling is analyzed, and the friction coefficient database for sample data is established through the friction coefficient calculation model, which is used GWO-KELM neural network training friction coefficient prediction model, the Fleck rolling force prediction model based on the modified friction coefficient is established ultimately. A comparative analysis of prediction errors is conducted on three different specifications of strip steel using actual production data from a multifunctional 280 mm 20-high mill. The results show that the best performing MSE, RMSE, MAE, MAPE and R2, with values of 170.48, 13.06 kN, 9.01 kN, 3.30%, and 0.989, respectively. The accuracy of the modified rolling force prediction model is significantly improved, and the data scale of friction coefficient database can be continuously expanded, so the accuracy of the rolling force prediction model can be continuously improved.
引用
收藏
页码:1899 / 1908
页数:10
相关论文
共 39 条
[21]   Experimental and theoretical analysis of roll flattening in the deformation zone for ultra-mm strip rolling [J].
Ren, Zhongkai ;
Xiao, Hong ;
Liu, Xiao ;
Wang, Gaofei .
IRONMAKING & STEELMAKING, 2018, 45 (09) :805-812
[22]   Multi-objective optimization design of scramjet nozzle based on grey wolf optimization algorithm and kernel extreme learning machine surrogate model [J].
Tong, Shuhong ;
Guo, Mingming ;
Tian, Ye ;
Le, Jialing ;
Zhang, Dongqing ;
Zhang, Hua .
PHYSICS OF FLUIDS, 2024, 36 (02)
[23]  
Wang D. C., Journal of Plasticity Engineering
[24]   Research and Development Trend of Shape Control for Cold Rolling Strip [J].
Wang, Dong-Cheng ;
Liu, Hong-Min ;
Liu, Jun .
CHINESE JOURNAL OF MECHANICAL ENGINEERING, 2017, 30 (05) :1248-1261
[25]  
Wang Dongcheng, 2015, China Mechanical Engineering, V26, P2677, DOI 10.3969/j.issn.1004-132X.2015.19.021
[26]  
[王家琪 Wang Jiaqi], 2023, [钢铁, Iron and Steel], V58, P85
[27]  
[王立萍 Wang Liping], 2003, [中国机械工程, China Mechanical Engineering], V14, P918
[28]   Grey wolf optimization evolving kernel extreme learning machine: Application to bankruptcy prediction [J].
Wang, Mingjing ;
Chen, Huiling ;
Li, Huaizhong ;
Cai, Zhennao ;
Zhao, Xuehua ;
Tong, Changfei ;
Li, Jun ;
Xu, Xin .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2017, 63 :54-68
[29]  
[王晓 Wang Xiao], 2020, [机械工程材料, Materials for Mechanical Engineering], V44, P62
[30]   A Data-Driven Multiobjective Dynamic Robust Modeling and Operation Optimization for Continuous Annealing Production Process [J].
Wang, Yao ;
Wang, Xianpeng ;
Dong, Zhiming ;
Wang, Zan .
ISIJ INTERNATIONAL, 2020, 60 (06) :1225-1236