Microstructure and corrosion resistance of stainless steel produced by bypass coupling twin-wire indirect arc additive manufacturing

Wu, Dongting; An, Qi; Du, Fuxin; Matsuda, Kenji; Tang, Yingwen; Zou, Yong

doi:10.1007/s00170-021-08343-1

Cited by 6 publications

(15 citation statements)

References 30 publications

(31 reference statements)

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“…The study observed 33 a transition in droplet transfer behavior for BC-TWIAW samples from a short-circuit mode to a stable spray mode as the welding current increased. Notably, the most stable droplet transfer occurred at a welding current of 200 A.…”

Section: (4) Corrosion Of Wire Arc Additively Manufactured Steelsmentioning

confidence: 91%

“…59 (4.2.8) Process modification. In a study, We et al 33 employed bypass coupling twin-wire indirect arc welding (BC-TWIAW) for arc additive manufacturing of 18-8 stainless steel. In twin-wire indirect arc welding, the negative and positive poles of the DC power source are connected to the two feeding wires.…”

Section: (4) Corrosion Of Wire Arc Additively Manufactured Steelsmentioning

confidence: 99%

“…Consequently, this reduced point defects in the passive film, resulting in an enhanced localized corrosion resistance. 33 (4.3) Influence of microstructure and post-fabrication treatment (4.3.1) Microstructural imperfections and secondary phases. It was reported 29 that microstructural imperfections, such as lack of fusion, impurities, and porosity, can have a detrimental impact on the corrosion fatigue (CF) behavior of WAAM-fabricated carbon steel.…”

Section: (4) Corrosion Of Wire Arc Additively Manufactured Steelsmentioning

confidence: 99%

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Corrosion response of steels fabricated through arc directed energy deposition additive manufacturing: a review

Morshed-Behbahani,

Nasiri

2024

Mater. Horiz.

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Section: (4) Corrosion Of Wire Arc Additively Manufactured Steelsmentioning

confidence: 91%

Section: (4) Corrosion Of Wire Arc Additively Manufactured Steelsmentioning

confidence: 99%

Section: (4) Corrosion Of Wire Arc Additively Manufactured Steelsmentioning

confidence: 99%

See 1 more Smart Citation

Corrosion response of steels fabricated through arc directed energy deposition additive manufacturing: a review

Morshed-Behbahani,

Nasiri

2024

Mater. Horiz.

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“…This approach resulted in a 20% less height deviation with an enhanced material utilisation by 13% due to the bypass current generation, which reduced the heat input onto the substrate. In a significant study, Wu et al (2022) developed a bypass coupled twin-wire indirect arc welding for AM. This approach was found to be 30% more efficient than conventional GMAW-WAAM.…”

Section: Introductionmentioning

confidence: 99%

Non-transferring arc and wire additive manufacturing: microstructure, mechanical properties and bulk texture evolution of deposits

Pattanayak,

Sahoo,

Sahoo

et al. 2024

RPJ

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Purpose This study aims to demonstrate a modified wire arc additive manufacturing (AM) named non-transferring arc and wire AM (NTA-WAM). Here, the build plate has no electrical arc attachment, and the system’s arc is ignited between tungsten electrode and filler wire. Design/methodology/approach The effect of various deposition conditions (welding voltage, travel speed and wire feed speed [WFS]) on bead characteristics is studied through response surface methodology (RSM). Under optimum deposition condition, a single-bead and thin-layered part is fabricated and subjected to microstructural, tensile testing and X-ray diffraction study. Moreover, bulk texture analysis has been carried out to illustrate the effect of thermal cycles and tensile-induced deformations on fibre texture evolutions. Findings RSM illustrates WFS as a crucial deposition parameter that suitably monitors bead width, height, penetration depth, dilution, contact angle and microhardness. The ferritic (acicular and polygonal) and lath bainitic microstructure is transformed into ferrite and pearlitic micrographs with increasing deposition layers. It is attributed to a reduced cooling rate with increased depositions. Mechanical testing exhibits high tensile strength and ductility, which is primarily due to compressive residual stress and lattice strain development. In deposits, ϒ-fibre evolution is more resilient due to the continuous recrystallisation process after each successive deposition. Tensile-induced deformation mostly favours ζ and ε-fibre development due to high strain accumulations. Originality/value This modified electrode arrangement in NTA-WAM suitably reduces spatter and bead height deviation. Low penetration depth and dilution denote a reduction in heat input that enhances the cooling rate.

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“…Currently, the “Teach-Playback” mode [ 3 ] is widely used in robot welding, resulting in a more favorable working environment; it increases welding efficiency to some extent and ensures the consistency of welding quality. However, the “Teach-Playback” mode still relies on the worker’s judgment for determining the welding start point and welding mode.…”

Section: Introductionmentioning

confidence: 99%

YOLO-Weld: A Modified YOLOv5-Based Weld Feature Detection Network for Extreme Weld Noise

Gao

Fan

et al. 2023

Sensors

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Weld feature point detection is a key technology for welding trajectory planning and tracking. Existing two-stage detection methods and conventional convolutional neural network (CNN)-based approaches encounter performance bottlenecks under extreme welding noise conditions. To better obtain accurate weld feature point locations in high-noise environments, we propose a feature point detection network, YOLO-Weld, based on an improved You Only Look Once version 5 (YOLOv5). By introducing the reparameterized convolutional neural network (RepVGG) module, the network structure is optimized, enhancing detection speed. The utilization of a normalization-based attention module (NAM) in the network enhances the network’s perception of feature points. A lightweight decoupled head, RD-Head, is designed to improve classification and regression accuracy. Furthermore, a welding noise generation method is proposed, increasing the model’s robustness in extreme noise environments. Finally, the model is tested on a custom dataset of five weld types, demonstrating better performance than two-stage detection methods and conventional CNN approaches. The proposed model can accurately detect feature points in high-noise environments while meeting real-time welding requirements. In terms of the model’s performance, the average error of detecting feature points in images is 2.100 pixels, while the average error in the world coordinate system is 0.114 mm, sufficiently meeting the accuracy needs of various practical welding tasks.

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Microstructure and corrosion resistance of stainless steel produced by bypass coupling twin-wire indirect arc additive manufacturing

Cited by 6 publications

References 30 publications

Corrosion response of steels fabricated through arc directed energy deposition additive manufacturing: a review

Corrosion response of steels fabricated through arc directed energy deposition additive manufacturing: a review

Non-transferring arc and wire additive manufacturing: microstructure, mechanical properties and bulk texture evolution of deposits

YOLO-Weld: A Modified YOLOv5-Based Weld Feature Detection Network for Extreme Weld Noise

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