Hole Expansion Test and Characterization of High-Strength Hot-Rolled Steel Strip

In this study, the effect of the material anisotropies of hot-rolled high-strength steel sheet on localized deformation behavior in hole expansion test has been investigated experimentally. First, the hole expansion test with the circular hole has been conducted to investigate the effect of anisotropies of material properties on the localized deformation behavior around the hole edge. Next, the hole expansion test with the oval hole has been conducted to investigate the effect of the major axis direction of the oval hole on the localized deformation behavior around the hole edge. As a result, it was clarified that the effect of anisotropies of r-value and n-value on the localized deformation behavior is strong, especially the anisotropy of n-value.

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ARMCO FORMABLE 70 HR

Alloy Digest ◽

10.31399/asm.ad.sa0359 ◽

1979 ◽

Vol 28 (5) ◽

Keyword(s):

Nonmetallic Inclusions ◽

High Strength ◽

Heat Treating ◽

Rolled Steel ◽

Low Carbon ◽

Controlled Processing ◽

High Strength Level ◽

Hot Rolled ◽

Hot Rolled Steel ◽

Special Composition

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.

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Numerical analysis of temperature and residual stresses in hot-rolled steel strip during cooling in coils

Archives of Civil and Mechanical Engineering ◽

10.1016/j.acme.2017.11.002 ◽

2018 ◽

Vol 18 (2) ◽

pp. 659-668 ◽

Cited By ~ 3

Author(s):

Szczepan Witek ◽

Andrij Milenin

Keyword(s):

Numerical Analysis ◽

Residual Stresses ◽

Steel Strip ◽

Rolled Steel ◽

Hot Rolled ◽

Hot Rolled Steel

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X-SDD: A New Benchmark for Hot Rolled Steel Strip Surface Defects Detection

Symmetry ◽

10.3390/sym13040706 ◽

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.

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