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Research on pipeline intelligent welding based on combined line structured lights vision sensing: a partitioned time–frequency-space image processing algorithm.
- Source :
-
International Journal of Advanced Manufacturing Technology . Oct2024, Vol. 134 Issue 11/12, p5463-5479. 17p. - Publication Year :
- 2024
-
Abstract
- By applying vision sensing to realize online intelligent control on the pipeline welding process, the welding efficiency and qualification rate of pipeline girth butt joint during on-site construction of long-distance oil and gas pipelines can be effectively improved. During the pipeline external welding process, based on the designed combined line structured lights vision sensor (C-LSLVS), a partitioned time–frequency-space (PTFS) image processing algorithm is proposed to extract the laser centerline for the deformed laser lines image of double-V composite groove. The algorithm first utilizes information in the time domain to remove random interference from the image, and then, based on Radon transform (RT) and discrete Fourier transform (DFT), other stationary interferences can be eliminated and laser line characteristics can be enhanced by back-projection reconstruction in the frequency domain and space domain, so the stable and accurate extraction of laser centerline can be achieved under different situations. Subsequently, the theoretical analysis and error calculation of the pipeline groove sizes and related parameters solution model based on the local plane fitting method are carried out, and high-precision calculations of the parameters are completed. Finally, an intelligent pipeline welding system based on the C-LSLVS is constructed. In the intelligent welding experiment, the maximum detection error of the pipeline welding groove sizes did not exceed 0.20 mm, and the weld seam tracking deviation did not exceed 0.25 mm. The welding results show that the system has important application value for the development of pipeline intelligent welding. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02683768
- Volume :
- 134
- Issue :
- 11/12
- Database :
- Academic Search Index
- Journal :
- International Journal of Advanced Manufacturing Technology
- Publication Type :
- Academic Journal
- Accession number :
- 180107186
- Full Text :
- https://doi.org/10.1007/s00170-024-14368-z