Papers by Keyword: Seam Tracking

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Authors: Yong Qiang Wu, Zhong Hu Yuan, Jia Han Wang
Abstract: Kinematics model of welding robot is built in the paper. An improved fuzzy controller (Fuzzy-P) for welding robot mobile platform is designed based on analyzing seam tracking control system. The domain of fuzzy control should not be set too big in order to make system smooth, but the system must respond rapidly. P control can respond rapidly. When weld seam deviation is big,it adopts P control while seam deviation is small, it adopts Fuzzy-P control. The simulation result shows that the improved controller is effective for 45°broken line; the welding torch is able to track the welding seam well.
Authors: Yu Xiang Hong, Bo Hong, Jian Liu, Xiang Wen Li
Abstract: In order to solve the problems of seam tracking for automation welding of large welding structure, this paper has respectively built mathematic model for length of magnetic-control arc according to two typical forms of weld pass in multi-pass welding for V-groove seam. By using Matlab/Simulink to analyze the transformation discipline of welding currents, a method for deviation acquisition based on magnetic-control arc sensing for multi-pass welding is proposed. Welding experiments demonstrates that the method proposed can effectively resolve the dilemma of deviation acquisition during multi-pass welding for thick plates with V-groove seam.
Authors: Min Ho Park, Qian Qian Wu, Cheol Kyun Park, Jong Pyo Lee, Ill Soo Kim
Abstract: With the development of economy, the seam tracking technology for arc welding becomes one of the major research tasks in the manufacturing area with robots. In this study, the objectives aim to develop an intelligent and cost-effective algorithm based on the laser vision sensor for image processing in Gas Metal Arc (GMA) welding. Images of welded seam were captured from the CCD camera. These images were then processed by the algorithms in the proposed image processing. To optimize the effective image process, popular algorithms in use were verified, compared and finally selected for every step in the image processing. Moreover, owing to the simple interactive environment and abundant toolboxes, MATLAB was employed to realize those algorithms, which offer a sample for engineers to achieve the goal of algorithm developed by this new but easier approach. Finally, weld seam images obtained with different welding environments were processed to enhance the proposal validity, and it’s proved to have significant effect of getting rid of the variable noises to extract the feature points and centerline for seam tracking in GMA welding and is capable for industrial application.
Authors: Bo Chen, Ji Cai Feng
Abstract: With the exploration of marine sources becoming more and more important, underwater welding is widely needed. Because of the special working condition, underwater weld seam tracking technology is urgently needed, for the automation control of the underwater welding process is the inevitable development trend because of the rigorous environment. This paper used ultrasonic sensor to monitor the weld seam position in underwater wet welding process, and signal process algorithm was developed to obtain the weld seam information, experiment results showed that this method could detect the weld seam shape correctly, this load the foundation for further automatically controlling the welding process.
Authors: Bo Chen, Chuan Bao Jia, Ji Cai Feng
Abstract: Weld automation is the development trend of underwater welding, and underwater weld seam tracking is one of the key technologies in weld automation. This paper used active visual sensor to automatically monitor the weld seam in underwater wet weld process, and image processing algorithms were developed to automatically obtain the weld torch deviation, then the weld torch was adjusted automatically according to the deviation obtained by the image, experiment results showed that this method could be used in underwater wet welding.
Authors: Yi Bo Deng, Hua Zhang, Guo Hong Ma
Abstract: During the practical researches and applications based on passive visual servo, weld torch was often moved to the weld seam initial position manually by human or by the means of accurate mounting devices, which will often cause low efficiency, as well as a increase in manufacturing cost. To solve this problem an algorithm was presented in this paper to identify the initial position of the Aluminum plate weld seam. It’s totally based on passive visual servo system without utilizing any auxiliary optical equipment. This algorithm combined a variety of DIP techniques and approaches, accurate initial position could be extracted successfully. More images were processed by this method to confirm the validity. With the realization of auto-identification precision of the weld jig can be reduced dramatically and the degree of the automation and flexibility can be improved greatly.
Authors: C. H. Kim, W.S. Yoo, S.-J. Na
Authors: Hong Tang Chen, Hai Chao Li, Hong Ming Gao, Lin Wu
Abstract: Welding seam tracking precision is a key factor influencing welding quality for master-slave robot remote welding system. However, it does not satisfy the welding requirement due to significant noises. To eliminate the influence of noises upon the seam tracking precision and improve the seam tracking precision, a master-slave robot remote welding system was built and Kalman filtering (KF) was applied to the seam tracking process. The experimental results show that the KF eliminated the influence of noises upon the seam tracking precision and improved the seam tracking precision.
Authors: Hai Lin Hu, Jing Li, Fang Li, Wei Zhu, He Qiang Pang
Abstract: He sensing of the weld pool and controlling of torch at the center of the groove are important problems in back welding of GMAW (Gas Metal Arc Welding) for pipeline, furthermore, the gap of the groove perhaps is varied, which needs an intelligent control strategy to obtain the high welding quality. Fuzzy neural network control method based on BP algorithm is proposed in this paper, from the module of image processing, the corresponding gap location and width can be obtained. Then determine corresponding swing width and speed when weld gap is varied by the network fuzzy inference and calculating Euclidean distance for GMAW variable gap backing welding process. Experiment results show that the designed control method can improve the welding quality compared with traditional fixed swing and the traditional auto swing.
Authors: Feng Xin Gao
Abstract: This paper presents a vision-based curve-shaped seam tracking system. The measurement of the curve trajectory is based on a camera that is installed ahead of the end effector of the robot. Effective image processing method is designed to extract the feature points of the curve. Then a fuzzy controller is proposed to control the position of the robot to track the curve. To improve the performance of the controller, a self-tuning mechanism is added to the controller. Finally, experiments are fully conducted to verify the effectiveness of the proposed method.
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