A measurement method of weld temperature and tool positioning during friction stir welding process

Mi, Songtao; Zhang, Yucun; Fu, Xiang; Yan, Fang; Shen, Jiuli

doi:10.1007/s40194-021-01067-6

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…Therefore, the input heat should be reduced to prevent the occurrence of cracks and increase the toughness of the weld seam. The main function of cover welding is to fully cover the groove after filling welding, thereby ensuring the quality of welding formation [24][25]. The filling area is mainly affected by many parameters such as the current size of the welding machine.…”

Section: Startmentioning

confidence: 99%

Multi-Layer and Multi-Channel Dynamic Routing Planning and Initial Point Positioning of Weld Seam Based on Machine Vision

Li,

Zheng

2023

IEEE Access

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The welding process for medium and thick plates typically involves multi-layering and multichanneling, but its quality and reliability require further improvement. Therefore, this study introduces the convolution neural network algorithm to establish a deep learning model for weld seam recognition. Additionally, the structured light imaging method is used to accurately position the V-shaped weld groove. Meanwhile, a machine vision based multi-layer and multi-pass dynamic routing planning algorithm was also studied and designed, and a contour feature point recognition algorithm for the filling layer was developed. Thus, dynamic routing planning is achieved. It is demonstrated that the difference between the coordinates acquired by the deep learning model and the ideal region decreases steadily and reaches a minimum of (200,80). The confidence level of weld seam detection gradually increases with the adjustment of the welding robot, reaching a maximum of 98%. The confidence level of the detected feature points reaches 100%. In the meantime, the remaining height of the fusion after welding is 2.5mm. There are no negative phenomena present on the surface of the weld seam, meeting the necessary process requirements. Such discrepancies as undercut, incomplete penetration, slag inclusion, and porosity are absent. It shows that the welding technology based on machine vision has strong feasibility, effectively improves the automation level and efficiency of welding technology, and provides reliable technical support for the development of modern machine vision welding technology.

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Section: Startmentioning

confidence: 99%

Multi-Layer and Multi-Channel Dynamic Routing Planning and Initial Point Positioning of Weld Seam Based on Machine Vision

Li,

Zheng

2023

IEEE Access

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“…The effect of precipitate dissolution and grain growth could be improved by postweld heat treatment (PWHT) [4][5][6], but practical limits to heat treating large subassemblies to the entire aircraft kept this solution from implementation. Therefore, fusion welding solutions were no longer considered, and new, solid-state welding solutions were investigated [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Microstructure evaluation of dissimilar AA2024 and AA7050 aluminum joints made by corner stationary-shoulder friction stir welding

et al. 2022

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Fillet welds made by the corner stationary-shoulder friction stir welding process between AA7050-T7451 and AA2024-T4 sheets were characterized using different metallographic techniques and mechanical testing. Robotic welds of an aircraft’s skin-to-internal stiffeners were examined using Barker’s electrolytic and Keller’s etching techniques and correlated with electron backscattered diffraction results and energy-dispersive microprobe analysis. The composition and grain orientation maps and material flow lines demonstrated excellent weld quality in spite of the apparent inhomogeneities in the stir zone where mechanical mixing was complete. Welded joint efficiencies were in the 85–92% of the base metals and were acceptable in terms of resistance to crack initiation and propagation and corrosion resistance, even with softening of the heat-affected zones. It was concluded that several optical and electron microscopy techniques are needed to characterize these dissimilar aluminum welds fully and that post-weld mechanical and thermal treatments could even further improve their quality.

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Computational Fluid Dynamics Approach for Frictional Heat Flow Analysis During Friction Stir Welding Process

2022

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A measurement method of weld temperature and tool positioning during friction stir welding process

Cited by 6 publications

References 19 publications

Multi-Layer and Multi-Channel Dynamic Routing Planning and Initial Point Positioning of Weld Seam Based on Machine Vision

Multi-Layer and Multi-Channel Dynamic Routing Planning and Initial Point Positioning of Weld Seam Based on Machine Vision

Microstructure evaluation of dissimilar AA2024 and AA7050 aluminum joints made by corner stationary-shoulder friction stir welding

Computational Fluid Dynamics Approach for Frictional Heat Flow Analysis During Friction Stir Welding Process

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