Abstract
In this paper, an initial point alignment method of narrow weld using laser vision sensor is presented on the basis of the relationship between the feature point of laser stripe and initial point. The whole initial point alignment process contains two stages. At the first stage, the initial point image is captured, and the image coordinates of the feature point of laser stripe and initial point are obtained. At the second stage, according to the relationship between the feature point of laser stripe and initial point, the three-dimensional (3D) coordinates of initial point could be determined to achieve initial point alignment. The initial point alignment method mainly includes vision sensing and motion control two parts. Firstly, a new laser vision sensor with a uniform LED surface light source is developed to capture the high signal-to-noise ratio (SNR) image including narrow weld, and the feature point of laser stripe and initial point are detected using the image processing method. Secondly, initial point alignment control system including feature verification and controller is designed to achieve initial point alignment control. Finally, a series of initial point alignment experiments of straight and curve narrow weld are conducted to test the performance of the proposed method. Experimental results indicate the alignment error is less than previous methods, which could be used in automatic welding process.
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Chen SB, Chen XZ, Qiu T, Li JQ (2005) Acquisition of weld seam dimensional position information for arc welding robot based on vision computing. J Intell Robot Syst 43(1):77–97
Gao X, Mo L, Xiao Z, Chen X, Katayama S (2016) Seam tracking based on Kalman filtering of micro-gap weld using magneto-optical image. Int J Adv Manuf Technol 83(1–4):21–32
Le J, Zhang H, Xiao Y (2016) Circular fillet weld tracking in GMAW by robots based on rotating arc sensors. Int J Adv Manuf Technol 88(9–12):1–11
Umeagukwu C, Maqueira B, Lambert R (1989) Robotic acoustic seam tracking: system development and application. IEEE Trans Indust Electron 36(3):338–348
Bae KY, Park JH (2006) A study on development of inductive sensor for automatic weld seam tracking. J Mater Process Technol 176(1):111–116
Gu WP, Xiong ZY, Wan W (2013) Autonomous seam acquisition and tracking system for multi-pass welding based on vision sensor. Int J Adv Manuf Technol 69(1–4):451–460
Xu D, Fang ZJ, Chen HY, Yan ZG, Tan M (2012) Compact visual control system for aligning and tracking narrow butt seams with CO2 gas-shielded arc welding. Int J Adv Manuf Technol 62(9):1157–1167
Fang ZJ, Xu D, Tan M (2011) A vision-based self-tuning fuzzy controller for fillet weld seam tracking. IEEE/ASME Trans Mechatron 16(3):540–550
Xu Y, Lv N, Fang G, Du SF, Zhao WJ, YE Z, Chen SB (2017) Welding seam tracking in robotic gas metal arc welding. J Mater Process Technol 248:18–30
Lü X Q, Zhang K, Wu YX (2017) The seam position detection and tracking for the mobile welding robot. Int J Adv Manuf Technol 88(5–8):2201–2210
Lin T, Chen HB, Li WH, Chen SB (2009) Intelligent methodology for sensing, modeling, and control of weld penetration in robotic welding system. Ind Robot 36(6):585–593
Cayo EH, Alfaro SCA (2009) A non-intrusive GMA welding process quality monitoring system using acoustic sensing. Sensors 9(9):7150–7166
Fan CJ, Lv FL, Chen SB (2009) Visual sensing and penetration control in aluminum alloy pulsed GTA welding. Int J Adv Manuf Technol 42(1–2):126–137
Lv N, Zhong J, Chen H, Lin T, Chen SB (2014) Real-time control of welding penetration during robotic GTAW dynamical process by audio sensing of arc length. Int J Adv Manuf Technol 74(1–4):235–249
Zhang S, Hu S, Wang Z (2016) Weld penetration sensing in pulsed gas tungsten arc welding based on arc voltage. J Mater Process Technol 229:520–527
Sheng J, Cai Y, Li F, Hua X (2017) Online detection method of weld penetration based on molten pool morphology and metallic vapor radiation for fiber laser welding. Int J Adv Manuf Technol 92(1–4):1–15
Lashkia V (2001) Defect detection in X-ray images using fuzzy reasoning. Image Vis Comput 19(5):261–269
Schuster GJ, Doctor SR, Bond LJ (2004) A system for high-resolution, nondestructive, ultrasonic imaging of weld grains. IEEE Trans Instrum Meas 53(6):1526–1532
Yang L, Li E, Long T, Fan J, Mao Y, Fang Z, Liang Z (2018) A welding quality detection method for arc welding robot based on 3D reconstruction with SFS algorithm. Int J Adv Manuf Technol 94(1–4):1209–1220
Chu HH, Wang ZY (2016) A vision-based system for post-welding quality measurement and defect detection. Int J Adv Manuf Technol 86(9–12):1–8
Li Y, Li YF, Wang QL, Xu D, Tan M (2010) Measurement and Defect Detection of the Weld Bead Based on Online Vision Inspection. IEEE Trans Instrum Meas 59(7):1841–1849
Xu L, Cao MY, Wang HX, Collier M (2008) A method to locate initial welding position of container reinforcing plates using structured-light. In: Proceedings of the 27th Chinese control conference, Kunming, Yunan, pp 310–314
Wang NF, Shi XD, Zhang XM (2017) Recognition of initial welding position based on structured-light for arc welding robot. In: International conference on intelligent robotics and applications. Springer, Cham, pp 564–575
Ding Y, Huang W, Kovacevic R (2016) An on-line shape-matching weld seam tracking system. Robot Comput-Integr Manuf 42:103–112
Chen XZ, Chen SB, Lin T, Lei YC (2006) Practical method to locate the initial weld position using visual technology. Int J Adv Manuf Technol 30(7):663–668
Wei SC, Ma HB, Lin T, Chen SB (2010) Autonomous guidance of initial welding position with single camera and double positions method. Sensor Rev 30(1):62–68
Chen XZ, Chen SB (2010) The autonomous detection and guiding of start welding position for arc welding robot. Ind Robot 37(1):70–78
Fan JF, Jing FS, Fang ZJ, Liang ZZ (2015) A simple calibration method of structured light plane parameters for welding robots. In: Proceedings of the 35th Chinese control conference, Chendu, pp 6127–6132
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This work was supported by the National Natural Science Foundation of China under Grant 61573358.
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Fan, J., Jing, F., Yang, L. et al. An initial point alignment method of narrow weld using laser vision sensor. Int J Adv Manuf Technol 102, 201–212 (2019). https://doi.org/10.1007/s00170-018-3184-2
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DOI: https://doi.org/10.1007/s00170-018-3184-2