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

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 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

Simulation Study on Heating Process of Hot Rolled Steel Strip for X80M Pipeline Steel

2018 ◽  
Vol 913 ◽  
pp. 349-354
Author(s):  
Jian Gang Wang ◽  
Hao Wang ◽  
Yan Ping Bao ◽  
Min Wang ◽  
Guo Jing
Keyword(s):  
Pipeline Steel ◽  
Steel Strip ◽  
Alloy Element ◽  
Industrial Test ◽  
Heating Process ◽  
Rolled Steel ◽  
X80 Pipeline Steel ◽  
Austenite Grain Size ◽  
Hot Rolled ◽  
Hot Rolled Steel

The simulation experiments of heating process for X80 pipeline steel were carried out to investigate the influence of austenite grain size and micro alloy element solution. The results showed that a reasonable industrial production process of slab heating control system was developed, and the guidance of good industrial test could be provided for the acquisition of reasonable microstructure of 22mm thickness X80 pipeline steel hot rolled steel strip.

The Onset of the Austenite to Bainite Phase Transformation for Different Cooling Paths and Steel Compositions

2016 ◽  
Vol 716 ◽  
pp. 368-375 ◽  
Author(s):  
Aarne Pohjonen ◽  
Mahesh Somani ◽  
Juha Pyykkönen ◽  
Joni Paananen ◽  
David Porter
Keyword(s):  
Activation Energy ◽  
Phase Transformation ◽  
Hot Rolling ◽  
Steel Strip ◽  
Cooling Water ◽  
Water Cooling ◽  
Rolled Steel ◽  
Time Required ◽  
Hot Rolled ◽  
Hot Rolled Steel

We present calculations of austenite to bainite phase transformation start for different cooling paths and for different steel compositions and a method to estimate the cooling water required to cool a steel strip to desired temperatures during water cooling line after industrial hot rolling. We also quantitatively compare how different alloying elements affect the phase transformation activation energy and the time required for the transformation to start and proceed to the extent that it can be detected with dilatometer. This analysis can be used for aid when designing suitable cooling paths for hot rolled steel products. The calculations of the activation energy can be used as input in more detailed microstructure models.

Sign in / Sign up

Export Citation Format

Share Document