Reduction Behavior of Oxide Scales Containing Different Structures on the Surface of Hot‐Rolled Steel Strip in Gas Mixture H 2 /Ar at 400–625 °C

2019 ◽  
Vol 91 (1) ◽  
pp. 1900333
Author(s):  
Zhi-Feng Li ◽  
Hao Wang ◽  
Ye Gao ◽  
Guang-Ming Cao ◽  
Zhen-Yu Liu
Keyword(s):  
Steel Strip ◽  
Gas Mixture ◽  
Rolled Steel ◽  
Oxide Scales ◽  
Reduction Behavior ◽  
Hot Rolled ◽  
Hot Rolled Steel

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.

Texture Descriptors for Automatic Classification of Surface Defects of the Hot-Rolled Steel Strip

2021 ◽  
pp. 251-260
Author(s):  
Virginia Riego del Castillo ◽  
Lidia Sánchez-González ◽  
Alexis Gutiérrez-Fernández
Keyword(s):  
Surface Defects ◽  
Steel Strip ◽  
Automatic Classification ◽  
Rolled Steel ◽  
Hot Rolled ◽  
Hot Rolled Steel

scholarly journals Heat Transfer during Multiple Jet Impingement on the Top Surface of Hot Rolled Steel Strip

2008 ◽  
Vol 79 (12) ◽  
pp. 938-946 ◽  
Author(s):  
K.V. Jondhale ◽  
M.A. Wells ◽  
M. Militzer ◽  
V. Prodanovic
Keyword(s):  
Heat Transfer ◽  
Steel Strip ◽  
Jet Impingement ◽  
Rolled Steel ◽  
Hot Rolled ◽  
Hot Rolled Steel

Influence of oxide scales on the corrosion behaviors of B510L hot-rolled steel strips

2016 ◽  
Vol 23 (7) ◽  
pp. 769-778 ◽  
Author(s):  
Cheng Man ◽  
Chao-fang Dong ◽  
Hui-bin Xue ◽  
Kui Xiao ◽  
Xiao-gang Li ◽  
...  
Keyword(s):  
Rolled Steel ◽  
Oxide Scales ◽  
Steel Strips ◽  
Corrosion Behaviors ◽  
Hot Rolled ◽  
Hot Rolled Steel

scholarly journals Modelling of Temperature-Dependent Growth Kinetics of Oxide Scale on Hot-Rolled Steel Strip

2012 ◽  
Vol 13 (1) ◽  
pp. 219-223 ◽  
Author(s):  
Xianglong Yu ◽  
Zhengyi Jiang ◽  
Dongbin Wei ◽  
Xiaodong Wang ◽  
Quan Yang
Keyword(s):  
Oxide Scale ◽  
Growth Kinetics ◽  
Steel Strip ◽  
Rolled Steel ◽  
Temperature Dependent ◽  
Dependent Growth ◽  
Hot Rolled ◽  
Hot Rolled Steel ◽  
Kinetics Of
Sign in / Sign up

Export Citation Format

Share Document