The cross-sectional profile of hot-rolled silicon steel strips has a great impact on the transverse thickness difference of cold-rolled strips. The genetic algorithm - Levenberg Marquardt - backpropagation neural network model was developed for the analysis of heredity effect of the cross-sectional profile of hot-rolled strip. The feature importance was analysed based on random forest and extreme gradient boosting methods. Furthermore, the influence law was analysed by data visualization. The coupling term of the strip crown and thickness , the edge drop and the coupling term of the strip wedge and thickness are recommended as key indexes of the hot-rolled silicon steel strip. As the transverse thickness difference must be controlled below 7 μm, the corresponding , , should be controlled no more than 60, between -10∼10 μm and no more than 7.5 , respectively. Meanwhile, the requirement for the cross-sectional profile indexes is synchronous.
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References
1.
MaXBWangDCLiuHMet al.Large concave roll technology for hot rolled silicon steel. Ironmak Steelmak. 2016;45:66–75.
2.
CaoJGLiuSJZhangJet al.ASR work roll shifting strategy for schedule-free rolling in hot wide strip mills. J Mater Process Technol. 2011;211:1768–1775.
3.
CaoJGWeiGCZhangJet al.VCR and ASR technology for profile and flatness control in hot strip mills. J Cent South Univ Technol. 2008;15:264–270.
4.
WangXCYangQSunYZ.Rectangular section control technology for silicon steel rolling. J Iron Steel Res Int. 2015;22:185–191.
5.
YaritaIKitahamaMHirutaTet al.Transverse thickness profile control in hot and cold strip rolling by tapered-crown work roll shifting (K-WRS) mill. SEAISI Q. 1998;27:26–34.
6.
ZhouXMZhangQDWangCSet al.Automative edge drop control system of UCMW cold mill and its optimization. Iron Steel. 2007;42:56–59.
7.
SunWQYangQShaoJet al.Edge drop control technique of silicon steel for UCM tandem cold rolling mills. J Univ Sci Technol Beijing. 2010;31:1340–1345.
8.
HeARShaoJSunWQet al.Transverse thickness deviation control of non-oriented silicon steel during cold rolling. J. Mech Eng. 2011;47:25–30.
9.
ZhouXMWuYFMaoYJ.Neural network inverse decoupling control of flatness and edge drop for tandem cold rolling mill. Adv Mater Res. 2012;562-564:1977–1981.
10.
ZhouXMZhangQDWangCSet al.Edge drop and flatness decoupling control on UCMW cold mill. Iron Steel. 2007;42:55–57.
11.
MaXBWangDCLiuHM.Coupling mechanism of control on strip profile and flatness in single stand universal crown reversible rolling mill. Steel Res Int. 2017;88:1600495/10.
12.
JelaliMWolffAPetersHet al. Improved control in cold rolling by use of information on incoming hot-rolled cross-section profile and camber, Technical steel research report, in European Commission; 2002.
13.
AtakaM.Rolling technology and theory for the last 100 years: the contribution of theory to innovation in strip rolling technology. ISIJ Int. 2015;55:89–102.
14.
HeARShaoJWangXCet al.Research and application of approximate rectangular section control technology in hot strip mills. J Iron Steel Res Int. 2021;28:279–290.
15.
WangWJHouJXYangQet al.Forecast of each export proportion crown in hot rolling based on neural network. Heavy Machine. 2015;06:27–31.
16.
MaHW.Calculation of unit crown distribution of hot-rolled strip considering transverse flow coefficient. Steel Rolling. 2009;26:13–14+17.
17.
XuJZGongDYZhangWCet al.Model building of the initial crown effect rate in 4-high mill. J Mater Sci Technol. 2005;21: 165–169.
18.
XieLHeARLiuC.A rapid calculation method for predicting roll deformation of six-high rolling mill. J Iron Steel Res Int. 2018;25:901–909.
19.
WangTXiaoHZhaoTYet al.Improvement of 3-D FEM coupled model on strip crown in hot rolling. J Iron Steel Res Int. 2012;19:14–19.
20.
HuYGongDYJiangZYet al.Effect of initial crown on shape of hot-rolled strip. J Iron Steel Res Int. 2009;16:32–34.
21.
ZhuHTTieuAKDippenaarRJet al.Effect of hot coil profile containing ridges on ridge-buckle defects of cold rolled thin strip. Int J Mater Form. 2009;3:21–27.
22.
AltinkayaHOrakIMEsenI.Artificial neural network application for modeling the rail rolling process. Expert Syst Appl. 2014;41:7135–7146.
23.
DengJFSunJPengWet al.Application of neural networks for predicting hot-rolled strip crown. Appl Soft Comput. 2019;78:119–131.
24.
JiYFSongLBSunJet al.Application of SVM and PCA-CS algorithms for prediction of strip crown in hot strip rolling. J Cent South Univ. 2021;28:2333–2344.
25.
SunJDengJFPengWet al.Strip crown prediction in hot rolling process using random forest. Int J Precis Eng Man. 2021;22:301–311.
26.
WangZHMaGSGongDYet al.Application of mind evolutionary algorithm and artificial neural networks for prediction of profile and flatness in hot strip rolling process. Neural Process Lett. 2019;50:2455–2479.
27.
SikdarSKumariS.Neural network model of the profile of hot-rolled strip. Int J Adv Manuf Technol. 2008;42:450–462.
28.
WuQTangQHLiWGet al.Strip steel thickness prediction based on GA-BP neural network and sensitivity analysis. J Wuhan Univ Sci Technol. 2018;41:32–36.
29.
LiGLChenWLiuYJet al.Impact analysis for section profile of hot-rolled silicon steel. J Iron Steel Res. 2014;26:33–36.
30.
ZhangFQLiuBRKangYLet al.Study on pass genetic factor if crown of non-oriented silicon steel strip rolled on 20 high Sendzimir cold rolling mill. Iron Steel. 2005;40:40–45.
31.
MaXBWangDCLiuHMet al.Influence of profile indicators of hot-rolled strip on transverse thickness difference of cold-rolled silicon steel. Metall Res Technol. 2019;116:105/15.
32.
ChengYM.Same plate difference forecasts model for silicon steel based on hot rolling process parameters. Shandong Metall. 2020;42:36–40.
