Visual sensing image processing and feature information extraction for arc welding
ZHANG Tianyi, ZHU Zhiming, ZHU Chuanhui, SUN Bowen
Key Laboratory for Advanced Materials Processing Technology of Ministry of Education, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
Abstract:Visual sensing is an effective means for obtaining arc-welding characteristic information, such as the position and pose of the welding torch, the shape and size of the welding groove, for closed-loop feedback control in intelligent arc welding. The paper describes a visual sensing image processing and feature extraction method for arc welding. A multi-source sensor was developed based on the fusion of visual information with the effect of gravity. The hardware and image preprocessing algorithm are optimized to reduce the interference of the strong arc light, spatter, and other effects on the CCD image. The algorithm then uses the edge extraction based on a Canny operator or the skeleton thinning algorithm based on iterative erosion. The two algorithms separately process the CCD image of the welding groove collected by the multi-source sensor to extract the laser lines, the laser line intersection coordinates and the laser line bending points coordinates caused by the welding groove. Comparison of the feature information extraction speeds and recognition accuracies of the two algorithms shows that the edge extraction algorithm based on the Canny operator can provide real-time weld seam tracking during arc welding.
[1] 范俊峰, 景奉水, 方灶军. 基于视觉传感的焊缝跟踪技术研究现状和发展趋势[J]. 热加工工艺, 2017, 46(5):6-10, 14. FAN J F, JING F S, FANG Z J. Research status and development trend of welding seam tracking technology based on vision sensors[J]. Hot Working Technology, 2017, 46(5):6-10, 14. (in Chinese) [2] 吴林, 陈善本. 智能化焊接技术[M]. 北京:国防工业出版社, 2000. WU L, CHEN S B. Intelligent technologies for welding[M]. Beijing:National Defense Industry Press, 2000. (in Chinese) [3] 张瑞雪. 多线激光传感器Ⅴ型焊缝轨迹识别[D]. 南昌:南昌大学, 2018. ZHANG R X. Recognition of V-shaped weld trajectory with multi-line laser vision sensor[D]. Nanchang:Nanchang University, 2018. (in Chinese) [4] SHEN H Y, WU J, LIN T, et al. Arc welding robot system with seam tracking and weld pool control based on passive vision[J]. The International Journal of Advanced Manufacturing Technology, 2008, 39(7-8):669-678. [5] KONG M, CHEN S B. Al alloy weld pool control of welding robot with passive vision[J]. Sensor Review, 2009, 29(1):28-37. [6] 刘超, 邵文军, 黄禹, 等. 激光焊接中窄拼缝被动光视觉检测算法[J]. 小型微型计算机系统, 2019, 40(4):798-801. LIU C, SHAO W J, HUANG Y, et al. Weld seam detection method based on passive vision sensor in laser welding[J]. Journal of Chinese Computer Systems, 2019, 40(4):798-801. (in Chinese) [7] 石玗, 汪海涛, 薛诚, 等. 采用激光线光源的焊接坡口间隙视觉检测[J]. 兰州理工大学学报, 2009, 35(6):29-32. SHI Y, WANG H T, XUE C, et al. Visual detection of welding groove gap with linear laser source[J]. Journal of Lanzhou University of Technology, 2009, 35(6):29-32. (in Chinese) [8] 李明利, 刘占民. 焊接坡口激光检测图像处理及跟踪信息的提取[J]. 焊接学报, 2005, 26(5):31-35. LI M L, LIU Z M. Image processing and tracing data collection for welding groove laser detection[J]. Transactions of the China Welding Institution, 2005, 26(5):31-35. (in Chinese) [9] 李忠虎, 郭蕾, 闫俊红, 等. 线结构光光条中心提取算法研究[J]. 内蒙古科技大学学报, 2019, 38(3):252-257. LI Z H, GUO L, YAN J H, et al. Research on extraction algorithm of line structured light stripe center[J]. Journal of Inner Mongolia University of Science and Technology, 2019, 38(3):252-257. (in Chinese) [10] 孙小亮, 贾剑平, 叶艳辉, 等. 结构光在阴极板焊缝自动跟踪系统中的应用[J]. 有色冶金设计与研究, 2020, 41(增刊1):13-16. SUN X L, JIA J P, YE Y H, et al. Application and practice of structured light in automatic seam tracking system of cathode plate[J]. Nonferrous Metals Engineering & Research, 2020, 41(S1):13-16. (in Chinese) [11] 孙博文, 朱志明, 郭吉昌, 等. 基于组合激光结构光的视觉传感器检测算法及图像处理流程优化[J]. 清华大学学报(自然科学版), 2019, 59(6):445-452. SUN B W, ZHU Z M, GUO J C, et al. Detection algorithms and optimization of image processing for visual sensors using combined laser structured light[J]. Journal of Tsinghua University(Science and Technology), 2019, 59(6):445-452. (in Chinese) [12] 齐欣. 数字图像的存储方式及传输途径[J]. 照相机, 2004(7):42-43. QI X. Storage and transmission of digital image[J]. Camera, 2004(7):42-43. (in Chinese) [13] 张安定, 衣华鹏, 崔青春.《遥感原理》研究性教学的探索与实践[J]. 测绘通报, 2005(12):59-61. ZHANG A D, YI H P, CUI Q C. Searching and practice in the investigating teaching of remote sensing course[J]. Bulletin of Surveying and Mapping, 2005(12):59-61. (in Chinese) [14] 高浩军, 杜宇人. 中值滤波在图像处理中的应用[J]. 电子工程师, 2004, 30(8):35-36. GAO H J, DU Y R. The application of median filtering on image processing[J]. Electronic Engineer, 2004, 30(8):35-36. (in Chinese) [15] 丁怡心, 廖勇毅. Gauss模糊算法优化及实现[J]. 现代计算机(专业版), 2010(8):76-77, 100. DING Y X, LIAO Y Y. Optimization and implementation of Gaussian blur algorithm[J]. Modern Computer, 2010(8):76-77, 100. (in Chinese) [16] 汪涛, 成孝刚, 李德志, 等. 基于霍夫变换与角点检测的叶脉特征提取算法[J]. 计算机技术与发展, 2019, 29(11):159-162. WANG T, CHENG X G, LI D Z, et al. A feature extraction algorithm for leaf vein based on hough transform and corner detection[J]. Computer Technology and Development, 2019, 29(11):159-162. (in Chinese) [17] 王国宏, 李林, 于洪波. 基于点集合并的修正Hough变换TBD算法[J]. 航空学报, 2017, 38(1):203-213. WANG G H, LI L, YU H B. A modified Hough transform TBD algorithm based on point set merging[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(1):203-213. (in Chinese)