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
2017 ◽  
Vol 42 (50) ◽  
pp. 29921-29928 ◽  
Author(s):  
Dejian Ding ◽  
Hao Peng ◽  
Wangjun Peng ◽  
Yaowei Yu ◽  
Guangxin Wu ◽  
...  

2016 ◽  
Vol 43 (10) ◽  
pp. 739-743 ◽  
Author(s):  
C. Guan ◽  
J. Li ◽  
N. Tan ◽  
S.-G. Zhang

2015 ◽  
Vol 277 ◽  
pp. 151-159 ◽  
Author(s):  
Xianglong Yu ◽  
Zhengyi Jiang ◽  
Jingwei Zhao ◽  
Dongbin Wei ◽  
Ji Zhou ◽  
...  

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.


2014 ◽  
Vol 39 (27) ◽  
pp. 15116-15124 ◽  
Author(s):  
Chuang Guan ◽  
Jun Li ◽  
Ning Tan ◽  
Yong-Quan He ◽  
Shu-Guang Zhang

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

2019 ◽  
Vol 66 (5) ◽  
pp. 613-620
Author(s):  
Jiaxing Cai ◽  
Xuequn Cheng ◽  
Baijie Zhao ◽  
Linheng Chen ◽  
Yi Fan ◽  
...  

Purpose The purpose of this paper is to understand the process of failure of scale and the corrosion resistance of scale to the substrate in an atmospheric environment. Design/methodology/approach The corrosion behaviour of X65 pipeline steel with different types of oxide scale was analysed using the natural environment exposure corrosion test, scanning electron microscopy analysis, electrochemical corrosion polarization curve test and other methods in a warehouse environment. Findings The results of this research show that one type of oxide scale, which is rough, has an uneven microstructure, and exhibits weak adhesion to the matrix, does not protect the substrate from corrosion. Conversely, the uniform, dense oxide scale, which exhibits strong adhesion to the matrix, provides effective protection to the steel. However, as the corrosion develops, the corrosion rate of the substrate tends to accelerate, especially when the structure of the oxide scale is damaged to a certain extent. Originality/value The corrosion mechanism of the oxide scale on hot rolled steel in an atmospheric environment has been proposed.


Sign in / Sign up

Export Citation Format

Share Document