An increase in the surface quality of hot-rolled strip

Metallurgist ◽  
1963 ◽  
Vol 7 (5) ◽  
pp. 223-224
Author(s):  
M. A. Benyakovskii ◽  
O. A. Mel'nikov ◽  
L. N. Chukhlova ◽  
S. K. Glukhov
Keyword(s):  
Surface Quality ◽  
Rolled Strip ◽  
Hot Rolled Strip ◽  
Hot Rolled

scholarly journals A ResNet50-Based Method for Classifying Surface Defects in Hot-Rolled Strip Steel

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2359
Author(s):  
Xinglong Feng ◽  
Xianwen Gao ◽  
Ling Luo
Keyword(s):  
Classification Accuracy ◽  
Surface Defects ◽  
Rolled Strip ◽  
Defect Classification ◽  
Original Solution ◽  
Automotive Manufacturing ◽  
Strip Steel ◽  
Hot Rolled Strip ◽  
Hot Rolled

Hot-rolled strip steel is widely used in automotive manufacturing, chemical and home appliance industries, and its surface quality has a great impact on the quality of the final product. In the manufacturing process of strip steel, due to the rolling process and many other reasons, the surface of hot rolled strip steel will inevitably produce slag, scratches and other surface defects. These defects not only affect the quality of the product, but may even lead to broken strips in the subsequent process, seriously affecting the continuation of production. Therefore, it is important to study the surface defects of strip steel and identify the types of defects in strip steel. In this paper, a scheme based on ResNet50 with the addition of FcaNet and Convolutional Block Attention Module (CBAM) is proposed for strip defect classification and validated on the X-SDD strip defect dataset. Our solution achieves a classification accuracy of 94.11%, higher than more than a dozen other compared deep learning models. Moreover, to adress the problem of low accuracy of the algorithm in classifying individual defects, we use ensemble learning to optimize. By integrating the original solution with VGG16 and SqueezeNet, the recognition rate of oxide scale of plate system defects improved by 21.05 percentage points, and the overall defect classification accuracy improved to 94.85%.

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.

Research on causes and mitigation measures of bright band on hot rolled strip

2013 ◽  
Vol 41 (3) ◽  
pp. 187-192 ◽  
Author(s):  
D. F. Guo ◽  
A. R. He ◽  
J. Shao
Keyword(s):  
Mitigation Measures ◽  
Rolled Strip ◽  
Hot Rolled Strip ◽  
Bright Band ◽  
Hot Rolled

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.

Continuous and discrete control of the thickness of hot-rolled strip

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 ◽  
...  
Keyword(s):  
Discrete Control ◽  
Rolled Strip ◽  
Hot Rolled Strip ◽  
Hot Rolled
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