Citation: | YU Hongyu, LIU Zhihui, ZHANG Chengrui, CHEN Geng, GAO Tao. Positioning method for laser welding systems based on side-axis vision[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(9): 42-49, 102. DOI: 10.12073/j.hjxb.20230926002 |
In response to the suboptimal calibration results in laser welding, as well as significant discrepancies between the positioning accuracy and pixel equivalence, a camera calibration method and positioning approach based on side-axis vision were proposed on the basis of a self-developed laser welding system in the laboratory. The method involved the calibration of intrinsic parameters to correct lens distortion and the calibration of extrinsic parameters to establish the transformation relationship between the pixel coordinate system and the machine tool coordinate system. Additionally, a locally optimized algorithm for binary thresholding was designed to ensure accurate edge fitting of workpieces. The placement position and orientation of the workpiece in the system were determined through the identification of workpiece corner and the angle of one side, with the algorithm achieving a stable accuracy within 1 pixel, equivalent to a theoretical precision of 0.017 mm. Experimental testing of positioning accuracy involved the correction of recognized workpiece rotation angles. Prior to correction, the average errors in the x and y directions were 0.050 mm and 0.137 mm, respectively. After correction, the average errors were reduced to 0.029 mm in the x direction and 0.026 mm in the y direction, corresponding to multiples of pixel equivalence of 2.12 and 1.53, respectively. The actual errors exhibited minimal differences in multiples with pixel equivalence. The results indicated that this positioning method, while fully leveraging the camera's performance, demonstrated high algorithmic and positioning accuracy, enhancing processing efficiency and meeting practical production requirements.
[1] |
Wu Di, Zhang Peilei, Yu Zhishui, et al. Progress and perspectives of in-situ optical monitoring in laser beam welding: Sensing, characterization and modeling[J]. Journal of Manufacturing Processes, 2022, 75: 767 − 791. doi: 10.1016/j.jmapro.2022.01.044
|
[2] |
Aminzadeh A, Sattarpanah Karganroudi S, Meiabadi MS, et al. A survey of process monitoring using computer-aided inspection in laser-welded blanks of light metals based on the digital twins concept[J]. Quantum Beam Science, 2022, 6(2): 19. doi: 10.3390/qubs6020019
|
[3] |
You D Y, Gao X D, Katayama S. Review of laser welding monitoring[J]. Science and Technology of Welding and Joining, 2014, 19(3): 181 − 201. doi: 10.1179/1362171813Y.0000000180
|
[4] |
Fan X, Gao X, Liu G, et al. Research and prospect of welding monitoring technology based on machine vision[J]. The International Journal of Advanced Manufacturing Technology, 2021, 115: 3365 − 3391. doi: 10.1007/s00170-021-07398-4
|
[5] |
Xiao R, Xu Y, Xu F, et al. LSFP-Tracker: An autonomous laser stripe feature point extraction algorithm based on siamese network for robotic welding seam tracking[J]. IEEE Transactions on Industrial Electronics, 2024, 71(1): 1037 − 1048. doi: 10.1109/TIE.2023.3243265
|
[6] |
Dheeraj Dhruva Kumar, Fang Cheng, Zheng Yue, et al. Semi-supervised transfer learning-based automatic weld defect detection and visual inspection[J]. Engineering Structures, 2023, 292: 116580. doi: 10.1016/j.engstruct.2023.116580
|
[7] |
Thangavel S, Maheswari C, Priyanka E B. Passive machine vision-based defect classification in tungsten inert gas welding on SS304 using AI-based gradient descent algorithm[J]. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 2023, 237(5): 2102 − 2114.
|
[8] |
Pires J N, Loureiro A, Bolmsjo G. Welding robots: technology, system issues and application[M]. Springer Science & Business Media, 2006.
|
[9] |
马韵琪, 田明, 刘阳, 等. 基于改进Harris的消音壁亚像素级角点检测算法[J]. 长春理工大学学报(自然科学版), 2023, 46(1): 44 − 51.
Ma Yunqi, Tian Ming, Liu Yang, et al. Subpixel-level corner detection algorithm of sound-absorbing wall based on improved Harris[J]. Journal of Changchun University of Science and Technology(Natural Science Edition), 2023, 46(1): 44 − 51.
|
[10] |
支嘉斌, 曹云翔, 郭瑞, 等. 基于视觉的激光振镜精密焊接系统研究[J]. 制造业自动化, 2019, 41(6): 129 − 134. doi: 10.3969/j.issn.1009-0134.2019.06.033
Zhi Jiabin, Cao Yunxiang, Guo Rui, et al. Research on precision welding system of laser vibration mirror based on vision[J]. Manufacturing Automation, 2019, 41(6): 129 − 134. doi: 10.3969/j.issn.1009-0134.2019.06.033
|
[11] |
张志明. 基于OpenCV的激光焊接平台视觉定位系统的设计[D]. 广州: 广东工业大学, 2020.
Zhang Zhiming. Design of visual positioning system for laser welding platform based on OpenCV[D]. Guangzhou: Guangdong University of Technology, 2020.
|
[12] |
张浩然. 基于视觉定位算法的激光焊接系统设计与实现[D]. 武汉: 华中师范大学, 2022.
Zhang Haoran. Design and implementation of laser welding system based on visual positioning algorithm[D]. Wuhan: Central China Normal University, 2022.
|
[13] |
Song J, Li H, Chen Y, et al. A novel corner detection algorithm applied to vision-based alignment systems[C]//2022 8th International Conference on Control, Automation and Robotics (ICCAR), Xiamen, China, 2022: 426-430.
|
[14] |
Cai Wang, Wang Jianzhuang, Jiang Ping, et al. Application of sensing techniques and artificial intelligence-based methods to laser welding real-time monitoring: A critical review of recent literature[J]. Journal of Manufacturing Systems, 2020, 57: 1 − 18. doi: 10.1016/j.jmsy.2020.07.021
|
[15] |
朱铁爽, 张承瑞. 视觉辅助的激光激光头加工畸变校正及精度分析[J/OL]. 计算机集成制造系统: 1-18[2023-04-06].
Zhu Tieshuang, Zhang Chengrui. Distortion correction and accuracy analysis of laser galvanometer processing assisted by machine vision[J/OL]. Computer Integrated Manufacturing Systems: 1-18[2023-04-06].
|
[16] |
Yin Y S, Zhang C R, Zhu T S. Penetration depth prediction of infinity shaped laser scanning welding based on latin hypercube sampling and the neuroevolution of augmenting topologies[J]. Materials, 2021, 14(20): 5984. doi: 10.3390/ma14205984
|
[17] |
Yin Y S, Zhang C R, Zhu T S, et al. Development of a laser scanning machining system supporting on-the-Fly machining and laser power follow-up adjustment[J]. Materials, 2022, 15(16): 5479. doi: 10.3390/ma15165479
|
[18] |
刘智慧, 张承瑞, 李瑞珍. 基于机器视觉的光学镜片测量方法[J]. 电子测量技术, 2022, 45(1): 129 − 133.
Liu Zhihui, Zhang Chengrui, Li Ruizhen. Measuring method of optical lens size based on machine vision[J]. Electronic Measurement Technology, 2022, 45(1): 129 − 133.
|
[19] |
姚良振, 尹贻生, 张承瑞, 等. 基于功率随动控制拐角激光焊接质量优化[J]. 焊接学报, 2023, 44(5): 102 − 108. doi: 10.12073/j.hjxb.20220519001
Yao Liangzhen, Yin Yisheng, Zhang Chengrui, et al. Optimization of laser welding quality at corners based on power tracking control[J]. Transactions of the China Welding Institution, 2023, 44(5): 102 − 108. doi: 10.12073/j.hjxb.20220519001
|
[20] |
Huang Yong, Wang Boyang, Guo Jianghang, et al. The effect of activating fluxes on the cathode spots in the activating TIG welding[J]. China Welding, 2023, 32(1): 7 − 17.
|
[1] | XIN Chenglai, LI Ning, YAN Jiazhen. Investigation of brazing joints of Al2O3 ceramics to Kovar alloys by Ti+Nb/Mo metallization[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2018, 39(9): 45-48. DOI: 10.12073/j.hjxb.2018390222 |
[2] | WANG Peng, LI Qiang, GAO Zeng, CHENG Dongfeng, NIU Jitai. Investigation on magnetron sputtering Ti film and vacuum brazing of SiCp/Al composites with high volume fraction[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2015, 36(4): 43-46,82. |
[3] | WANG Zhenting, GAO Hongming, LIANG Gang, DING Yuanzhu. Microstructure and wear resistance of Ti-based composite coating reinforced by in-situ synthesized TiC and TiB2 particulates on surface of Ti6Al4V alloy with arc cladding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2014, 35(11): 51-54. |
[4] | ZONG Lin, LIU Zhengjun, GAO Hailiang, LI Lecheng. Research on wear resistance of compound material surfacing alloy with high vanadium content[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2011, (9): 41-44,48. |
[5] | LIU Zhengjun, SONG Xingkui, TANG Xingtao. Microstructure and wear resistance of dissimilar metal surfacing layer[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2011, (4): 99-102. |
[6] | CHANG Yunlong, LI Jingya, YANG Dianchen, JIN Wei. Influence of low frequency magnetic field on microstructure and wear resistance of submerged arc welding cladding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2011, (2): 37-40. |
[7] | WANG Zhenting, QIN Lifu. Microstructure and wear resistance of argon arc cladding Ni-Mo-Zr-WC-B4_C composite coating[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2009, (8): 13-16. |
[8] | LU Binfeng, LU Fenggui, TANG Xinhua, YAO Shun. Wear resistance of chromium carbides coating alloyed by vacaumelectron beam[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2008, (11): 77-80. |
[9] | WU Ping, JIANG En yong, ZHAO Ci, ZHOU Chang zhi, TANG Xi nan. Influence of laser cladding parameters on microstructure and wear-resistance of Ni-based alloy coatings[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2003, (2): 44-46,50. |
[10] | Tian Baohongl, Huang Jinliang, Wu Lei, Zhang Yimin, Zheng Shi'an. Effect of Rare-earth Oxide and Laser Remelting Technique on Wear Resistant of Composite Alloy Flame Spray Coating[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 1996, (2): 88-93. |
1. |
曲杰,王洋,王强,陈少波. 20G+316L复合钢管焊接工艺研究. 焊接技术. 2023(12): 94-99 .
![]() | |
2. |
刘永滨,杜华,徐哲. 钢质燃气管道贯穿孔固相摩擦塞焊修补方法研究. 机械制造文摘(焊接分册). 2018(06): 39-43 .
![]() |