Optimization Technology of Roll Contours during Skin-Pass Rolling Process of Hot-Rolled Strip

2019 ◽  
Vol 944 ◽  
pp. 237-246
Author(s):  
Guang Yi Song ◽  
Xiao Chen Wang ◽  
Quan Yang ◽  
Jian Wei Zhao

The hot-rolled strip skin-pass mill is the important technological equipment of finishing process in the hot rolling plant, and its role is to apply a slight thickness reduction of approximately 1% - 4% to the finished products cooled to room temperature to repair flatness defects and improve the surface quality and mechanical properties. In this study, the initial flat roll contours were optimized to solve some problems such as the serious and non-uniform wear of work rolls and the poor shape control abilities of the strip. A three-dimensional rolls–strip coupling model of hot-rolled strip skin-pass mill was established using the nonlinear finite element software ABAQUS. The effects of the roll contours on the contact pressure between rolls and the control effect of bending forces were analyzed, and a variable contact-length backup roll (VCR) contour configured with a positive crown contour for work rolls was proposed. Compared with the initial flat roll contours, the average replacement cycle of work rolls for the optimized roll contours increased from 465.4 tons to 701.3 tons, the non-uniform wear amount was reduced by nearly 15%, and the adjustment ratio of the negative bending forces decreased from -100% to -40% by industrial experiments in the 1580 mm single-stand four-high hot-rolled strip skin-pass mill.

2013 ◽  
Vol 690-693 ◽  
pp. 3295-3298
Author(s):  
Hai Fang Wang ◽  
Xiao Guang Ren ◽  
Yu Rong

Hot strip cooling temperature and its cooling rate is get by strip cooling system and coiling temperature of hot rolled strip is an important parameter on its performance index. It presents laminar cooling system of the 700 mm hot strip mill and the mill rolling process is present, the laminar cooling system is analyzed on the rolling theory and techniques. The laminar cooling system and its problems are present. The improvement programs of laminar cooling system are present, The control system of laminar cooling system is present combining feed-forward main control and feed-backward self-learning control, and measuring instruments is reinstall and water valves is improved. It could be a reference for the similar mill units and new building rolling mills.


2014 ◽  
Vol 941-944 ◽  
pp. 1696-1699
Author(s):  
Geng Sheng Ma ◽  
Fang Chen Yin ◽  
Xiao Yan Zhu ◽  
Wen Peng ◽  
Jian Zhao Cao ◽  
...  

The hot rolled strip thickness accuracy sometimes can not be guaranteed after a long time for waiting for slabs or other reasons. The reason is when rolling, the rollgap adaptive model has considered the thermal expansion of the roll. So when rolling is restarted, thermal expansion of the rolls must be cleared in order to accurately calculate the setup rollgap value. The finite element software ANSYS is used to calculate the temperature field and thermal expansion amount of the rolls in the rolling process. Application results show that this method can improve the accuracy of strip thickness.


2015 ◽  
Vol 112 (3) ◽  
pp. 305 ◽  
Author(s):  
Lian-yun Jiang ◽  
Guo Yuan ◽  
Jian-hui Shi ◽  
Yue Xue ◽  
Di Wu ◽  
...  

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 706
Author(s):  
Xinglong Feng ◽  
Xianwen Gao ◽  
Ling Luo

It is important to accurately classify the defects in hot rolled steel strip since the detection of defects in hot rolled steel strip is closely related to the quality of the final product. The lack of actual hot-rolled strip defect data sets currently limits further research on the classification of hot-rolled strip defects to some extent. In real production, the convolutional neural network (CNN)-based algorithm has some difficulties, for example, the algorithm is not particularly accurate in classifying some uncommon defects. Therefore, further research is needed on how to apply deep learning to the actual detection of defects on the surface of hot rolled steel strip. In this paper, we proposed a hot rolled steel strip defect dataset called Xsteel surface defect dataset (X-SDD) which contains seven typical types of hot rolled strip defects with a total of 1360 defect images. Compared with the six defect types of the commonly used NEU surface defect database (NEU-CLS), our proposed X-SDD contains more types. Then, we adopt the newly proposed RepVGG algorithm and combine it with the spatial attention (SA) mechanism to verify the effect on the X-SDD. Finally, we apply multiple algorithms to test on our proposed X-SDD to provide the corresponding benchmarks. The test results show that our algorithm achieves an accuracy of 95.10% on the testset, which exceeds other comparable algorithms by a large margin. Meanwhile, our algorithm achieves the best results in Macro-Precision, Macro-Recall and Macro-F1-score metrics.


2021 ◽  
Vol 2082 (1) ◽  
pp. 012016
Author(s):  
Xinglong Feng ◽  
Xianwen Gao ◽  
Ling Luo

Abstract A new Vision Transformer(ViT) model is proposed for the classification of surface defects in hot rolled strip, optimizing the poor learning ability of the original Vision Transformer model on smaller datasets. Firstly, each module of ViT and its characteristics are analyzed; Secondly, inspired by the deep learning model VGGNet, the multilayer fully connected layer in VGGNet is introduced into the ViT model to increase its learning capability; Finally, by performing on the X-SDD hot-rolled steel strip surface defect dataset. The effect of the improved algorithm is verified by comparison experiments on the X-SDD hot-rolled strip steel surface defect dataset. The test results show that the improved algorithm achieves better results than the original model in terms of accuracy, recall, F1 score, etc. Among them, the accuracy of the improved algorithm on the test set is 5.64% higher than ViT-Base and 2.64% higher than ViT-Huge; the accuracy is 4.68% and 1.36% higher than both of them, respectively.


Metallurgist ◽  
1974 ◽  
Vol 18 (6) ◽  
pp. 461-463
Author(s):  
M. A. Benyakovskii ◽  
E. P. Sergeev ◽  
B. V. Zdanovich ◽  
R. M. Ponomareva ◽  
S. N. Sidorovskii ◽  
...  

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