Welding Deviation Detection Algorithm Based on Extremum of Molten Pool Image Contour

• Published in: Chinese journal of mechanical engineering Vol. 29; no. 1; pp. 74 - 83
• Main Authors: Zou, Yong, Jiang, Lipei, Li, Yunhua, Xue, Long, Huang, Junfen, Huang, Jiqiang
• Format: Journal Article
• Language: English
• Published: Beijing Chinese Mechanical Engineering Society 2016; Optical Electromechanical Equipment Technology Key Laboratory of Beijing,Beijing Institute of Petrochemical Technology, Beijing 102617, China%Optical Electromechanical Equipment Technology Key Laboratory of Beijing,Beijing Institute of Petrochemical Technology, Beijing 102617, China%College of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China; College of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
• Subjects: Advanced Manufacturing and Intelligence Materials; Electrical Machines and Networks; Electronics and Microelectronics; Engineering; Engineering Thermodynamics; Heat and Mass Transfer; Instrumentation; Machines; Manufacturing; Mechanical Engineering; Power Electronics; Processes; Theoretical and Applied Mechanics; 偏差检测; 图像轮廓; 在线实时测量; 曲率极值点; 测算法; 焊缝跟踪控制; 熔化极气体保护焊; 熔池图像; molten pool contour; welding deviation; curvature extremum; welding seam tracking; gas metal arc welding(GMAW)
• ISSN: 1000-9345; 2192-8258
• DOI: 10.3901/CJME.2015.0908.110

The welding deviation detection is the basis of robotic tracking welding, but the on-line real-time measurement of welding deviation is still not well solved by the existing methods. There is plenty of information in the gas metal arc welding（GMAW） molten pool images that is very important for the control of welding seam tracking. The physical meaning for the curvature extremum of molten pool contour is revealed by researching the molten pool images, that is, the deviation information points of welding wire center and the molten tip center are the maxima and the local maxima of the contour curvature, and the horizontal welding deviation is the position difference of these two extremum points. A new method of weld deviation detection is presented, including the process of preprocessing molten pool images, extracting and segmenting the contours, obtaining the contour extremum points, and calculating the welding deviation, etc. Extracting the contours is the premise, segmenting the contour lines is the foundation, and obtaining the contour extremum points is the key. The contour images can be extracted with the method of discrete dyadic wavelet transform, which is divided into two sub contours including welding wire and molten tip separately. The curvature value of each point of the two sub contour lines is calculated based on the approximate curvature formula of multi-points for plane curve, and the two points of the curvature extremum are the characteristics needed for the welding deviation calculation. The results of the tests and analyses show that the maximum error of the obtained on-line welding deviation is 2 pixels（0.16 ram）, and the algorithm is stable enough to meet the requirements of the pipeline in real-time control at a speed of less than 500 mm/min. The method can be applied to the on-line automatic welding deviation detection.