Welding Seam Trajectory Recognition for Automated Skip Welding Guidance of a Spatially Intermittent Welding Seam Based on Laser Vision Sensor

Abstract

<jats:p>To solve the problems of low teaching programming efficiency and poor flexibility in robot welding of complex box girder structures, a method of seam trajectory recognition based on laser scanning displacement sensing was proposed for automated guidance of a welding torch in the skip welding of a spatially intermittent welding seam. Firstly, a laser scanning displacement sensing system for measuring angles adaptively is developed to detect corner features of complex structures. Secondly, a weld trajectory recognition algorithm based on Euclidean distance discrimination is proposed. The algorithm extracts the shape features by constructing the characteristic triangle of the weld trajectory, and then processes the set of shape features by discrete Fourier analysis to solve the feature vector used to describe the shape. Finally, based on the Euclidean distance between the feature vector of the test sample and the class matching library, the class to which the sample belongs is identified to distinguish the weld trajectory. The experimental results show that the classification accuracy rate of four typical spatial discontinuous welds in complex box girder structure is 100%. The overall processing time for weld trajectory detection and classification does not exceed 65 ms. Based on this method, the field test was completed in the folding special container production line. The results show that the system proposed in this paper can accurately identify discontinuous welds during high-speed metal active gas arc welding (MAG) welding with a welding speed of 1.2 m/min, and guide the welding torch to automatically complete the skip welding, which greatly improves the welding manufacturing efficiency and quality stability in the processing of complex box girder components. This method does not require a time-consuming pre-welding teaching programming and visual inspection system calibration, and provides a new technical approach for highly efficient and flexible welding manufacturing of discontinuous welding seams of complex structures, which is expected to be applied to the welding manufacturing of core components in heavy and large industries such as port cranes, large logistics transportation equipment, and rail transit.</jats:p>

Multiseam tracking with a portable robotic welding system in unstructured environments

“…The above dilemma drives the research of mobile-robotic welding system for the purpose of flexible deployment in the heavy industry. With respect to the mechanical design, the current mobile-robotic welding systems are mainly divided into three categories: the rail-based system [4][5][6][7], the welding carriage system [8][9][10] and hanging basket based system. The first, including the well-known commercail product, IGM welding robot, is limited to the applications in structured environments.…”

“…The first, including the well-known commercail product, IGM welding robot, is limited to the applications in structured environments. For example, a welding system based on a gantry mechanism with a guide rail is demonstrated to weld large containers [6]. The second is convenient to deploy because of its small size, however, it cannot weld complex spatially curved welds.…”

Online Extraction of Pose Information of 3D Zigzag-Line Welding Seams for Welding Seam Tracking

“…Vision-based 3D automatic welding has become a popular research topic [ 4 , 5 , 6 , 7 , 8 ]. The methods most commonly used to extract the pose information for 3D welding seams are the binocular vision-based method, which is based on RGB-D (RGB-Depth) sensors, and a method based on laser structured light.…”

All-position welding control system based on machine vision and nonlinear regression

“…12,13 The application of sampling vision sensing technology to weld space measurement tends to mature. [14][15][16] Deep learning technology is gradually applied to the field of robot welding for image analysis and prediction. 17,18 In practice, the images collected on site are accompanied by a lot of noise primarily resulting from welding arc light, weld spatter, and weld fume.…”