Research on robot weld seam recognition technology based on deep learning

When the robot performs real-time pipeline weld tracking, the weld image obtained by the camera will be interfered with by solid arc noise and spatter, which makes it challenging to ensure the stability of the weld quality of the pipeline. This paper proposes an automatic weld seam feature recognition algorithm based on an improved U-Net neural network. The method extracts the global features of the weld image through the backbone network after down-sampling and upsampling in the U-Net network, fuses the laser stripe information at multiple scales, and utilizes the feature enhancement module to obtain more explicit weld feature images. Experiments have shown that the accuracy can reach 99.17%.

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Publication Details

Published
2024-09-13
DOI
https://doi.org/10.1117/12.3040179
Citations
1
Primary Topic
Welding Techniques and Residual Stresses
Type
article
Field-Weighted Citation Impact
0.21
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article

Research on robot weld seam recognition technology based on deep learning

Yingxin Tao, Yang Zhang, Zhi-Yong Xing, diyu guan
1 citations
Welding Techniques and Residual Stresses
0.21
article

Research on robot weld seam recognition technology based on deep learning

Yingxin Tao, Yang Zhang, Zhi-Yong Xing, diyu guan
article en
1 citations

Abstract

When the robot performs real-time pipeline weld tracking, the weld image obtained by the camera will be interfered with by solid arc noise and spatter, which makes it challenging to ensure the stability of the weld quality of the pipeline. This paper proposes an automatic weld seam feature recognition algorithm based on an improved U-Net neural network. The method extracts the global features of the weld image through the backbone network after down-sampling and upsampling in the U-Net network, fuses the laser stripe information at multiple scales, and utilizes the feature enhancement module to obtain more explicit weld feature images. Experiments have shown that the accuracy can reach 99.17%.

Shenyang Institute of Engineering (CN), Energy Storage Systems (United States) (US)
Openalex Percentile: Top 52%
Welding Techniques and Residual Stresses
0.21
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