An Automatic Welding Seam Extraction Method for Butt Workpieces with Varying Seam Widths Based on DLP Vision

Linbo  Hao; Huaming  Wang; Zeyu  Sun; Yiming  Wang; Bin  Li; Shaohua  Jiang; Zhiqiang  Liu

doi:10.6180/jase.202601_29(1).0007

An Automatic Welding Seam Extraction Method for Butt Workpieces with Varying Seam Widths Based on DLP Vision

Linbo Hao¹This email address is being protected from spambots. You need JavaScript enabled to view it., Huaming Wang², Zeyu Sun¹, Yiming Wang¹, Bin Li¹, Shaohua Jiang², and Zhiqiang Liu²

¹Luoyang Institute of Science and Technology, Luoyang, 471023, China

²Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

Received: October 16, 2024
Accepted: March 19, 2025
Publication Date: May 1, 2025

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202601_29(1).0007

Planar butt seams are common in industrial sites, while the current seam extraction schemes are difficult to cope with butt seams with varying widths. Based on DLP (Digital Light Processing) vision technology, this paper proposes an automatic seam extraction method to solve this challenge. Firstly, we calculate different-granularity contour points based on the Alpha Shape algorithm, then extract seam feature points by calculating specific distance information. Secondly, we construct a type of 2D reference coordinate frame by analyzing the structural characteristics of the workpiece, then conduct a dimensionality reduction transformation on the feature points. Finally, we design an algorithm for curve fitting and discrete interpolation, which can generate the ordered interpolation points for welding. The experimental results demonstrate that the proposed seam extraction method can accurately, robustly, and efficiently extract the butt seams with varying widths.

Keywords: Butt seam; Seam extraction; DLP vision; Point cloud computing.

[1] H. Jahanshahi and Z. H. Zhu, (2024) “Review of ma chine learning in robotic grasping control in space application" Acta Astronautica: DOI: 10.1016/j.actaastro.2024.04.012.
[2] Q. Guo, Z. Liu, Z. Yang, Y. Jiang, Y. Sun, J. Xu, W. Zhao, W. Wang, W. Wang, Q. Ren, et al., (2024) “Development, challenges and future trends on the fabrication of micro-textured surfaces using milling technology" Journal of Manufacturing Processes 126: 285–331. DOI: https://doi.org/10.1016/j.jmapro.2024.07.112.
[3] Q. Guo, Z. Yang, J. Xu, Y. Jiang, W. Wang, Z. Liu, W. Zhao, and Y. Sun, (2024) “Progress, challenges and trends on vision sensing technologies in automatic/intelligent robotic welding: State-of-the-art review" Robotics and computer-integrated manufacturing 89: 102767. DOI: https://doi.org/10.1016/j.rcim.2024.102767.
[4] L. Hao, H. Wang, Y. Shen, X. Wang, and K. Xu, (2023) “A novel multi-seam extraction method for structured workpieces with medium-thick plates based on DLP vision" Measurement 223: 113808. DOI: 10.1016/j.measurement.2023.113808.
[5] Y. He, G. Ma, and S. Chen, (2021) “Autonomous decision-making of welding position during multipass GMAW with T-joints: A Bayesian network approach" IEEE Transactions on Industrial Electronics 69(4): 3909–3917. DOI: 10.1109/TIE.2021.3076710.
[6] B. Eren, M. H. Demir, and S. Mistikoglu, (2023) “Recent developments in computer vision and artificial intelligence aided intelligent robotic welding applications" The International Journal of Advanced Manufacturing Technology 126(11): 4763–4809. DOI: 10.1007/s00170-023-11456-4.
[7] H.N.M.Shah, M.Sulaiman, A. Z. Shukor, Z. Kamis, and A. AbRahman,(2018) “Butt welding joints recognition and location identification by using local thresholding" Robotics and Computer-Integrated Manufacturing 51: 181–188. DOI: 10.1016/j.rcim.2017.12.007.
[8] Y.-Z.Tian, H.-F. Liu, L. Li, W.-B. Wang, J.-C. Feng, F.-F. Xi, and G.-J. Yuan, (2020) “Robust identification of weld seam based on region of interest operation" Advances in Manufacturing 8: 473–485. DOI: 10.1007/s40436-020-00325-y.
[9] W. Fang, X. Xu, and X. Tian, (2022) “A vision-based method for narrow weld trajectory recognition of arc welding robots" The International Journal of Advanced Manufacturing Technology 121(11): 8039–8050. DOI: 10.1007/s00170-022-09804-x.
[10] N. Banafian, R. Fesharakifard, and M. B. Menhaj, (2021) “Precise seam tracking in robotic welding by an improved image processing approach" The International Journal of Advanced Manufacturing Technology 114(1): 251–270. DOI: 10.1007/s00170-021-06782-4.
[11] Y. Chen, Y. Shi, Y. Cui, and X. Chen, (2021) “Narrow gap deviation detection in Keyhole TIG welding using image processing method based on Mask-RCNN model" The International Journal of Advanced Manufacturing Technology 112: 2015–2025. DOI: 10.1007/s00170-020-06466-5.
[12] X. Chen, Q. Ma, Z. He, X. Sun, and Y. Ren, (2023) “Real-time detection and localization method for weld seam of narrow butt joint based on semantic segmentation" Measurement Science andTechnology35(3): 035205. DOI: 10.1088/1361-6501/ad16b9.
[13] X.Wang, T.Chen, Y.Wang, D.Zheng, X.Chen, and Z. Zhao, (2023) “The 3D narrow butt weld seam detection system based on the binocular consistency correction" Journal of Intelligent Manufacturing 34(5): 2321 2332. DOI: 10.1007/s10845-022-01927-y.
[14] W. Zhihui, X. Guisuo, L. Fang, W. Mengjun, H. Yizhen, C. Feng, and L. Wandong. “Point cloud ex traction of aircraft skin butt joint based on adaptive matching calibration algorithm”. In: 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE). IEEE. 2022, 136–141. DOI: 10.1109/CASE49997.2022.9926642.
[15] Z. Yuankai, J. Yong, T. Xincheng, X. Xiaolong, G. Yusen, and L. Min, (2023) “A point cloud-based welding trajectory planning method for plane welds" The International Journal of Advanced Manufacturing Technology 125(3): 1645–1659. DOI: 10.1007/s00170-022-10699-x.
[16] R. B. Rusu and S. Cousins. “3d is here: Point cloud library (pcl)”. In: 2011 IEEE international conference on robotics and automation. IEEE. 2011, 1–4. DOI: 10.1109/ICRA.2011.5980567.
[17] H. Edelsbrunner and E. P. Mücke, (1994) “Three dimensional alpha shapes" ACM Transactions On Graphics (TOG) 13(1): 43–72. DOI: 10.1145/174462.156635.
[18] D.W.Eggert, A. Lorusso, and R. B. Fisher, (1997) “Es timating 3-D rigid body transformations: a comparison of four major algorithms" Machine vision and applica tions 9(5): 272–290. DOI: 10.1007/s001380050048.
[19] C. Liu, H. Wang, Y. Huang, Y. Rong, J. Meng, G. Li, and G. Zhang, (2022) “Welding seam recognition and tracking for a novel mobile welding robot based on multi-layer sensing strategy" Measurement Science and Technology 33(5): 055109. DOI: 10.1088/1361-6501/ac3d06.
[20] M. Dinham and G. Fang, (2013) “Autonomous weld seam identification and localisation using eye-in-hand stereo vision for robotic arc welding" Robotics and Computer-Integrated Manufacturing 29(5): 288–301. DOI: 10.1016/j.rcim.2013.01.004.