scholarly journals Modeling the stress state of steel strip at roller levelling machine under cyclic alternating deformations

2020 ◽  
Vol 63 (10) ◽  
pp. 808-814
Author(s):  
K. A. Kotov ◽  
N. L. Bolobanova ◽  
D. V. Nushtaev

The final stage in the production of hot rolled steel is leveling on roller levellers under cyclic alternating deformation. When laser is cutting a sheet it may bend due to the release of residual stresses that are unevenly distributed over the volume. The majority of roller leveller models for calculating the process under cyclic alternating deformation does not provide an adequate assessment and prediction of residual stresses in a steel sheet. On the basis of finite element analysis, formation of residual stresses owing to roller levelling of hot rolled strip is disclosed. The implementation of a model of the levelling process was performed in SIMULIA Abaqus. Models are verificated by comparing forces under the rollers. We have experimentally confirmed the convergence of the simulation results with the measurements of the strip flatness obtained after sheets plasma cutting. It was found that after levelling, tensile longitudinal residual stresses remain on the upper surface of the sheet, compressive ones remain on the lower surface, stresses are zero in the middle in thickness, and the stress values are opposite in sign in the remaining parts of the section. It was established that the same parameters of the levelling process of different strength categories lead to different deviations of stresses. An increase in yield strength of the strip leads to an increase in the deviation of residual stresses along the strip thickness. The proposed method of simulation of roller levelling process should be used to study the stress-strain state of hot-rolled steel and to design improved strip levelling setting modes with minimal residual stress deviations.

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 ◽  
pp. 251-260
Author(s):  
Virginia Riego del Castillo ◽  
Lidia Sánchez-González ◽  
Alexis Gutiérrez-Fernández

2008 ◽  
Vol 79 (12) ◽  
pp. 938-946 ◽  
Author(s):  
K.V. Jondhale ◽  
M.A. Wells ◽  
M. Militzer ◽  
V. Prodanovic

2013 ◽  
Vol 43 (5) ◽  
pp. 313-316 ◽  
Author(s):  
S. M. Bel’skii ◽  
Yu. A. Mukhin ◽  
S. I. Mazur ◽  
A. I. Goncharov

2012 ◽  
Vol 13 (1) ◽  
pp. 219-223 ◽  
Author(s):  
Xianglong Yu ◽  
Zhengyi Jiang ◽  
Dongbin Wei ◽  
Xiaodong Wang ◽  
Quan Yang

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