WANG Wenkai, SHI Yu, ZHANG Gang, et al.Weld Seam Trajectories Offline Planning Based on Laser Vision Sensing[J].Electric Welding Machine, 2023, 53(9): 55-60.
WANG Wenkai, SHI Yu, ZHANG Gang, et al.Weld Seam Trajectories Offline Planning Based on Laser Vision Sensing[J].Electric Welding Machine, 2023, 53(9): 55-60. DOI: 10.7512/j.issn.1001-2303.2023.09.07.
Weld Seam Trajectories Offline Planning Based on Laser Vision Sensing
Laser vision sensing technology has become one of the mainstream welding seam tracking techniques due to its features of non-contact, high precision, and fast response. However, in the actual welding process, strong interferences such as arc light and spatter can lead to drift of laser vision feature points and failure in information extraction. To mitigate the interference caused by strong arc light and spatter during the high-current CO,2, gas shielded welding, an offline visual measurement method is employed to inspect the workpiece to be welded. The acquired images are processed through composition morphological filtering and Euclidean distance transform. Using the differential method, the groove position information is extracted. Subsequently, grouping averaging and interpolation methods are employed to obtain complete welding trajectory data, which is then transmitted to the lower-level controller for automated welding. Ultimately, a uniformly formed weld seam free from defects such as porosity and underfill is achieved.
CHEN H B,CHEN S B. Key Information Perception and Control Strategy of Intellignet Welding Under Complex Scene[J]. Acta Metallurgica Sinica, 2022, 58(04): 541-550.
Lei T, Huang Y, Shao W, et, al. A tactual weld seam tracking method in super narrow gap of thick plates[J]. Robotics and Computer-integrated Manufacturing,2020,62: 101864.
Baek D, Moon H S, Park S H. Development of an automatic orbital welding system with robust weaving width control and a seam-tracking function for narrow grooves[J]. The International Journal of Advanced Manufacturing Technology, 2017, 93(1): 767-777.
Liu W, Guan Z, Jiang X, et al. Research on the seam tracking of narrow gap P-GMAW based on arc sound sensing[J]. Sensors and Actuators, A. Physical, 2019, 292: 205-216.
QIN Z H,LI X W,ZHENG X J,et al. Seam recognition by magnetic control seam tracking sensor under asymmetric longitudinal magnetic field[J]. Transactions of The China Welding Institution,2023,44(5):84-94+134.
WU Y F, FU Q, QI J C. Automatic welding technology for circular seam of spherical tank based on passive vision sensor[J]. Electric Welding Machine, 2019, 49(9):91-94.
Wu K, Wang T, He J, et al. Autonomous seam recognition and feature extraction for multi-pass welding based on laser stripe edge guidance network[J]. The International Journal of Advanced Manufacturing Technology, 2020, 111(9): 2719-2731.
DONG J F, TANG D Y, WU D, et al. Research Status and Prospect of Laser Vision Weld Seam Tracking Image Processing Under Strong Noise[J].Electric Welding Machine, 2022, 52(12): 1-16.
Muhammad J, Altun H, Abo-Serie E. A robust butt welding seam finding technique for intelligent robotic welding system using active laser vision[J]. International Journal of Advanced Manufacturing Technology, 2018, 94(1): 13-29.
KONG M,ZHANG J,ZHANG L Z,et al. T-shaped fillet seam tracking method based on laser stripe visual sensor[J]. Electric Welding Machine, 2018, 48(10):101-104.
WANG S Q,ZHOU Y,CHEN H L,et al. Image processing system of welding seam of steel structure based on laser vision[J]. Transactions of The China Welding Institution, 2022, 43(02): 101-105+112+120.
Xiao R, Xu Y, Hou Z, et al. An adaptive feature extraction algorithm for multiple typical seam tracking based on vision sensor in robotic arc welding[J]. Sensors and Actuators A: Physical, 2019, 297: 111533.
Zou Y, Chen T, Chen X, et al. Robotic seam tracking system combining convolution filter and deep reinforcement learning[J]. Mechanical Systems & Signal Processing, 2022, 165: 108372.
Xu F, Zhang H, Xiao R, et al. Autonomous weld seam tracking under strong noise based on feature-supervised tracker-driven generative adversarial network[J]. Journal of Manufacturing Processes, 2022, 74: 151-167.