Mathematical model of profile shape identification of hot-rolled strip cross-section and distribution of drawings through cold-rolled strip width. Report 1

2018 ◽  
pp. 11-15
Author(s):  
V.A. Pimenov ◽  
◽  
S.M. Belskiy ◽  
E.V. Kuznetsova ◽  
F.N. Shkarin ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Cross Section ◽  
Strip Width ◽  
Rolled Strip ◽  
Shape Identification ◽  
Profile Shape ◽  
Hot Rolled Strip ◽  
Cold Rolled ◽  
Hot Rolled

Assessment of the actual shape of the hot-rolled strip cross-section contour. Part 2. Profile Classifier

2020 ◽  
pp. 33-37
Author(s):  
S. M. Belskiy ◽  
◽  
A. N. Shkarin ◽  
V. A. Pimenov ◽  
◽  
...  
Keyword(s):  
Cross Section ◽  
Middle Part ◽  
Characteristic Classes ◽  
Rolled Strip ◽  
Cross Sectional ◽  
Hot Rolled Strip ◽  
Cross Sectional Profile ◽  
Cold Rolled ◽  
Sectional Profile ◽  
Hot Rolled

The geometric parameters describing the features of the crosssectional profile of a hot-rolled strips do not give a complete picture of the flatness acquired by the cold-rolled strips rolled from these strips. An additional analysis, the results of which are presented in Message 1, showed that there are four characteristic classes of cross-sectional profiles of hot rolled strips that have a significant effect on the shape of the strips during cold rolling, three of which negatively affect the flatness of the cold rolled strips. The cross-sectional profiles of hot-rolled strips with a concave middle part and / or marginal thickenings lead to the appearance of edge waviness, peak-like cross-sectional profiles cause central warping. Therefore, the actual task is to determine the factual shape of cross-sectional profile. 6th order polynomials were used to digitalize and parameterize hot-rolled profile. As a result, we developed analytic function of the transverse profile, which keeps important information about its near-edge areas and features in the middle part. To assign a specific crosssectional profile of a hot-rolled strip to one of four characteristic classes of cross-sections, mathematical software was developed, called a classifier, and implemented with the programming environment R. To classify the profiles of the hot-rolled cross-section according to characteristic classes, a linear discriminant method was used as a machine learning method analysis. The result is an adequate mathematical model for recognizing the shape of the cross-sectional profile. The study was carried out with the financial support of the Russian Foundation for Basic Research within the framework of scientific project No. 19-38-90257.

Influence of the cross section of hot-rolled steel on the flatness of cold-rolled strip

2013 ◽  
Vol 43 (5) ◽  
pp. 313-316 ◽  
Author(s):  
S. M. Bel’skii ◽  
Yu. A. Mukhin ◽  
S. I. Mazur ◽  
A. I. Goncharov
Keyword(s):  
Cross Section ◽  
Rolled Strip ◽  
Rolled Steel ◽  
Cold Rolled ◽  
The Cross ◽  
Hot Rolled ◽  
Hot Rolled Steel

Influence of profile indicators of hot-rolled strip on transverse thickness difference of cold-rolled silicon steel

2018 ◽  
Vol 116 (1) ◽  
pp. 105
Author(s):  
Xiaobao Ma ◽  
Dongcheng Wang ◽  
Hongmin Liu ◽  
Shuai Zhang
Keyword(s):  
Measured Data ◽  
Silicon Steel ◽  
Rolled Strip ◽  
Hot Rolled Strip ◽  
Influence Model ◽  
Cold Rolled ◽  
Simulation Results ◽  
Hot Rolled ◽  
Control Criterion ◽  
Edge Drop

In order to evaluate the transverse thickness difference of cold-rolled strips according to the information of hot-rolled strips and scientifically guide the setting of the indicators of the hot-rolled silicon strip, the influence model about the relation of the transverse thickness difference of cold-rolled strip to the profile indicators of hot-rolled strip is established in this paper based on simulation results. The transverse thickness difference of cold-rolled strip predicted based on the influence model have strong correspondences to the measured data. Based on the influence model and the statistical analysis of the measured data, the control criterion of the profile indicators of hot-rolled silicon steel according to the requirements for the transverse thickness difference of cold-rolled strip are finally recommended. The simulation results show that the transverse thickness difference of cold-rolled strip is quadratic nonlinearly related to the wedge and crown of hot-rolled strip. The influence model and statistical data analysis indicate that reducing the edge-drop of hot-rolled strip is beneficial to restrain the transverse thickness difference of cold-rolled strip.

Quantitative study on the relationship between the transverse thickness difference of cold-rolled silicon strip and incoming section profile based on the mechanism-intelligent model

2021 ◽  
Vol 118 (3) ◽  
pp. 303
Author(s):  
Dongcheng Wang ◽  
Yanghuan Xu ◽  
Tongyuan Zhang ◽  
Xiaobao Ma ◽  
Hongmin Liu
Keyword(s):  
Neural Network ◽  
Operation Time ◽  
Quantitative Relationship ◽  
Rolled Strip ◽  
Hot Rolled Strip ◽  
Mechanism Model ◽  
Section Profile ◽  
Cold Rolled ◽  
Hot Rolled ◽  
The Relationship

Cold-rolled non-oriented silicon strip is widely used, and users have strict requirements for its transverse thickness difference. It is of great significance to study the quantitative relationship between the transverse thickness difference and incoming section profile of cold-rolled silicon strip and to formulate appropriate control indexes of the hot-rolled profile. To achieve the above purpose, this paper first proposes a method to describe the section profile of hot-rolled strip. A mechanism model for predicting the transverse thickness difference of cold-rolled silicon strip is established. Based on the characteristics of neural network transfer learning, the calculated results of the mechanism model are combined with actual production data, and the PSO-LM-BP neural network is trained by using the strategy of pre-training + retraining to obtain the mechanism-intelligence model for the prediction of the transverse thickness difference of cold-rolled silicon strip. The innovation of this paper is the combination of physical model and neural network. The prediction accuracy of the model is improved by two orders of magnitude on average, and the operation time is reduced. The relationship between the hot-rolled strip section crown, wedge and cold-rolled strip transverse thickness difference is quantitatively analysed, and the control strategy diagram of the key parameters of the hot-rolled section is finally obtained. The production of cold-rolled silicon strip with 1420 mm UCM shows that this strategy has a beneficial effect on the transverse thickness difference control of a cold-rolled strip.

Hot-rolled strip re-reddening temperature changing law for late ultra fast-cooling in a CSP line

2015 ◽  
Vol 112 (3) ◽  
pp. 305 ◽  
Author(s):  
Lian-yun Jiang ◽  
Guo Yuan ◽  
Jian-hui Shi ◽  
Yue Xue ◽  
Di Wu ◽  
...  
Keyword(s):  
Rolled Strip ◽  
Hot Rolled Strip ◽  
Ultra Fast Cooling ◽  
Fast Cooling ◽  
Hot Rolled

scholarly journals X-SDD: A New Benchmark for Hot Rolled Steel Strip Surface Defects Detection

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 706
Author(s):  
Xinglong Feng ◽  
Xianwen Gao ◽  
Ling Luo
Keyword(s):  
Surface Defect ◽  
Surface Defects ◽  
Steel Strip ◽  
Rolled Strip ◽  
Rolled Steel ◽  
Strip Surface ◽  
Hot Rolled Strip ◽  
Hot Rolled ◽  
Hot Rolled Steel ◽  
Detection Of Defects

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.

scholarly journals An improved Vision Transformer-based method for classifying surface defects in hot-rolled strip steel

2021 ◽  
Vol 2082 (1) ◽  
pp. 012016
Author(s):  
Xinglong Feng ◽  
Xianwen Gao ◽  
Ling Luo
Keyword(s):  
Surface Defect ◽  
Surface Defects ◽  
Steel Strip ◽  
Learning Ability ◽  
Rolled Strip ◽  
Strip Surface ◽  
Strip Steel ◽  
Hot Rolled Strip ◽  
Hot Rolled ◽  
Improved Algorithm

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.

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