Analysis of the Surface Defects in a Hot-Rolled Low-Carbon C–Mn Steel Plate

2017 ◽  
Vol 17 (3) ◽  
pp. 545-553 ◽  
Author(s):  
P. P. Sarkar ◽  
S. K. Dhua ◽  
S. K. Thakur ◽  
S. Rath
2018 ◽  
Vol 54 (4) ◽  
pp. 3438-3454 ◽  
Author(s):  
Y. Zou ◽  
Y. B. Xu ◽  
D. T. Han ◽  
Z. P. Hu ◽  
R. D. K. Misra ◽  
...  

1993 ◽  
Vol 90 (7-8) ◽  
pp. 917-922
Author(s):  
Y. Matsuda ◽  
M. Nishino ◽  
J. Ikeda

Alloy Digest ◽  
1972 ◽  
Vol 21 (8) ◽  

Abstract AISI 1015 is a low-carbon steel used in the annealed, cold-worked, hot-rolled or normalized condition for general purpose construction and engineering. It is also used for case-hardened components. It combines good machinability, good workability and good weldability. This datasheet provides information on composition, physical properties, hardness, elasticity, and tensile properties as well as fracture toughness. It also includes information on forming, heat treating, machining, joining, and surface treatment. Filing Code: CS-48. Producer or source: Carbon steel mills.


Alloy Digest ◽  
1990 ◽  
Vol 39 (4) ◽  

Abstract ASTM A710 is a low-carbon, precipitation hardening high-strength alloy steel plate. It is well suited to critical applications. This datasheet provides information on composition and tensile properties as well as fracture toughness. It also includes information on heat treating and joining. Filing Code: SA-446. Producer or source: Bethlehem Steel Corporation.


Alloy Digest ◽  
1986 ◽  
Vol 35 (8) ◽  

Abstract BethStar 60 steel plate is a high-strength product with a 60,000 psi minimum yield strength. It contains low carbon and low sulfur and has outstanding toughness, weldability and formability. It provides the design engineer with a an economical high-strength low-alloy (HSLA) grade that can be fabricated readily. Applications include weight-sensitive components subject to high stress such as frames for large off-highway haulers. This datasheet provides information on composition, physical properties, microstructure, elasticity, and tensile properties as well as fracture toughness. It also includes information on forming, heat treating, and joining. Filing Code: SA-421. Producer or source: Bethlehem Steel Corporation.


Alloy Digest ◽  
1979 ◽  
Vol 28 (5) ◽  

Abstract ARMCO FORMABLE 70 HR is a hot-rolled steel with excellent ductility, weldability and edge-tear resistance at a minimum yield strength of 70,000 psi (483 MPa). For this relatively high strength level, it has unusually good fabricating properties that are the result of closely controlled processing of a fully killed, low-carbon, vacuum-degassed, columbium-alloyed steel. This special composition and processing practice minimize harmful nonmetallic inclusions that hamper formability. Typical applications include automotive reinforcements, truck parts and construction components. This datasheet provides information on composition, physical properties, hardness, elasticity, and tensile properties as well as fatigue. It also includes information on corrosion resistance as well as forming, heat treating, machining, and joining. Filing Code: SA-359. Producer or source: Armco Inc., Eastern Steel Division.


2021 ◽  
Vol 9 (6) ◽  
pp. 604
Author(s):  
Du-Song Kim ◽  
Hee-Keun Lee ◽  
Woo-Jae Seong ◽  
Kwang-Hyeon Lee ◽  
Hee-Seon Bang

The International Maritime Organization has recently updated the ship emission standards to reduce atmospheric contamination. One technique for reducing emissions involves using liquefied natural gas (LNG). The tanks used for the transport and storage of LNG must have very low thermal expansion and high cryogenic toughness. For excellent cryogenic properties, high-Mn steel with a complete austenitic structure is used to design these tanks. We aim to determine the optimum welding conditions for performing Laser-MIG (Metal Inert Gas) hybrid welding through the MIG leading and laser following processes. A welding speed of 100 cm/min was used for welding a 15 mm thick high-Mn steel plate. The welding performance was evaluated through mechanical property tests (tensile and yield strength, low-temperature impact, hardness) of the welded joints after performing the experiment. As a result, it was confirmed that the tensile strength was slightly less than 818.4 MPa, and the yield strength was 30% higher than base material. The low-temperature impact values were equal to or greater than 58 J at all locations in the weld zone. The hardness test confirmed that the hardness did not exceed 292 HV. The results of this study indicate that it is possible to use laser-MIG hybrid welding on thick high-Mn steel plates.


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.


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