In Situ Observation of Microstructural and Inclusions Evolution in High-Strength Steel Deposited Metals with Various Rare Earth Pr Contents

Zhang, Tianli; Wang, Weiguang; Ma, Yan; Fang, Naiwen; Lin, Sanbao; Li, Zhuoxin; Kou, Sindo

doi:10.3390/ma15031257

Cited by 6 publications

(5 citation statements)

References 27 publications

(31 reference statements)

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“…c = σ(Conv2d(y)) (10) where Concat represents the splicing operation by channel, Conv2d represents the 2D convolution operation, and σ represents the sigmoid function.…”

Section: Dilated Convolution and Spatial Attention Fusion Module (Dsm)mentioning

confidence: 99%

“…As a metallic material [ 3 ], steel is widely used in manufacturing processes, such as brazing [ 4 , 5 , 6 ], laser welding [ 7 , 8 ], and additive manufacturing [ 9 ]. Surface defects in steel are often related to the microstructure changes during steel fabrication [ 10 ], and the interaction between alloying elements and microstructure can affect the formation of surface defects, thereby affecting the organization and mechanical properties of steels [ 11 , 12 ]. Steel surface defects may include cracks, bubbles, inclusions, scars, scratches.…”

Section: Introductionmentioning

confidence: 99%

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DAssd-Net: A Lightweight Steel Surface Defect Detection Model Based on Multi-Branch Dilated Convolution Aggregation and Multi-Domain Perception Detection Head

et al. 2023

Sensors

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During steel production, various defects often appear on the surface of the steel, such as cracks, pores, scars, and inclusions. These defects may seriously decrease steel quality or performance, so how to timely and accurately detect defects has great technical significance. This paper proposes a lightweight model based on multi-branch dilated convolution aggregation and multi-domain perception detection head, DAssd-Net, for steel surface defect detection. First, a multi-branch Dilated Convolution Aggregation Module (DCAM) is proposed as a feature learning structure for the feature augmentation networks. Second, to better capture spatial (location) information and to suppress channel redundancy, we propose a Dilated Convolution and Channel Attention Fusion Module (DCM) and Dilated Convolution and Spatial Attention Fusion Module (DSM) as feature enhancement modules for the regression and classification tasks in the detection head. Third, through experiments and heat map visualization analysis, we have used DAssd-Net to improve the receptive field of the model while paying attention to the target spatial location and redundant channel feature suppression. DAssd-Net is shown to achieve 81.97% mAP accuracy on the NEU-DET dataset, while the model size is only 18.7 MB. Compared with the latest YOLOv8 model, the mAP increased by 4.69%, and the model size was reduced by 23.9 MB, which has the advantage of being lightweight.

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“…c = σ(Conv2d(y)) (10) where Concat represents the splicing operation by channel, Conv2d represents the 2D convolution operation, and σ represents the sigmoid function.…”

Section: Dilated Convolution and Spatial Attention Fusion Module (Dsm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DAssd-Net: A Lightweight Steel Surface Defect Detection Model Based on Multi-Branch Dilated Convolution Aggregation and Multi-Domain Perception Detection Head

et al. 2023

Sensors

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“…In materials processing, the performance of a product is influenced by the chemical composition of the material [1][2][3][4][5][6][7][8], the processing method [9,10], the equipment, and the parameters [11]. Materials such as iron, copper, aluminum, and titanium have a wide range of applications in manufacturing, construction, electronics, and other industries.…”

Section: Introductionmentioning

confidence: 99%

Pre–Placed Metal Laser–Additive Manufacturing on Grade 5 Titanium Substrate: Layer Formation, Oxidation Resistance, and Wear Resistance

Lin,

Wang,

et al. 2023

Metals

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The laser parameters and additive materials were investigated for the laser–additive manufacturing of titanium alloys. A pre–placed metal–strip–based method for 3D printing titanium components was proposed. Before laser processing, the metal strips were fixed to a Grade 5 titanium substrate using resistance spot welding. The effect of the processing conditions (laser power: 1.5–3.0 kW; scanning speed: 3–9 mm·s−1; one or three layers; 1, 6, or 12 passes; Grade 2 and Grade 5 as additives) on the microstructures, oxidation resistance, and wear resistance of the as–printed samples was investigated. The results showed that the microstructure consisted of α′ martensite, oxygen–stabilized α, transformed β that contains coarse and fine acicular α, titanium oxides, and carbides. Cracks were observed, particularly near the upper surface of the three–layer samples. The cracks were suggested to have formed due to cumulative residual stresses and the formation of oxygen–stabilized α and α′ martensite that might cause embrittlement. Both oxidation and wear tests were conducted to verify the improved performance. After 55 h of oxidation, the as–printed samples showed mass gains of 0.029–0.035 g·cm−2, which were smaller than those of the substrate (0.039 g·cm−2).

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“…The increasing popularity of automotive lightweight design concepts has greatly promoted the application and extensively of ultra-high strength steels (UHSS) [1][2][3]. 22MnB5 steel is an ordinary steel with an ultimate tensile strength of 1.5GPa after hot stamping.…”

Section: Introductionmentioning

confidence: 99%

Improving the Microstructure and Mechanical Properties of Laser Welded Al-Si Coated 22MnB5/ Galvanized Steel Joint Added by Nickel

Zhang¹,

Qin²,

Qi³

2023

Preprint

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To weaken the harm of Al-Si coating and increase the strength of welded joint, variable thickness of Ni foil (Ni, an austenitic formation element) was added into the lap laser welding Al-Si coated 22MnB5 hot stamping steel/galvanized steel joints. The joints' weld appearance, microstructure, and mechanical properties were explored. The weld altered from X-shape to Y-shape with the increased thickness of Ni foil. During welding, Al-Si coating was melted and diluted into the welding pool, forming δ-ferrite (a rich-Al phase with low toughness and strength) in FZ and FB. This phase deteriorated the strength of the joints. After adding Ni, the amount and size of the δ-ferrite phase decreased. With a significant thickness of Ni foil, δ-ferrite would disappear. However, a new phase (FM, rich-Ni phase) probably formed except PM(a no or negligible Ni phase). The heat-affected zone (HAZ) on the side of 22MnB5 comprised a coarse martensite zone, refined martensite zone, martensite+ferrite zone, and tempered martensite zone from the FZ to the basic material. HAZ on the side of galvanized steel mainly contained ferrite and pearlite. After adding Ni foil, the strength of the joint was more than that without Ni. The maximum strength of the joint can be up to 679MPa because of the disappearance of δ-ferrite. Meanwhile, the toughness of the joint increased. The fracture mode was from three mixed fractures(cleavage, quasi-cleavage, and dimple) to one fracture(dimple).

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In Situ Observation of Microstructural and Inclusions Evolution in High-Strength Steel Deposited Metals with Various Rare Earth Pr Contents

Cited by 6 publications

References 27 publications

DAssd-Net: A Lightweight Steel Surface Defect Detection Model Based on Multi-Branch Dilated Convolution Aggregation and Multi-Domain Perception Detection Head

DAssd-Net: A Lightweight Steel Surface Defect Detection Model Based on Multi-Branch Dilated Convolution Aggregation and Multi-Domain Perception Detection Head

Pre–Placed Metal Laser–Additive Manufacturing on Grade 5 Titanium Substrate: Layer Formation, Oxidation Resistance, and Wear Resistance

Improving the Microstructure and Mechanical Properties of Laser Welded Al-Si Coated 22MnB5/ Galvanized Steel Joint Added by Nickel

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