| At present, 90% of the welding robots applied in manufacturing are primarily"teach and playback"robots, and few work in trajectory planning mode. However, welding encounters many variables, such as the errors of pre-machining, fitting of work-piece and in-process thermal distortions, which will change the gap size and seam position so as to affect welding quality.So real-time seam tracking technology could be used to improve tracking precision for those robots working in"teach and playback"or trajectory planning mode, especially for those work-pieces with large distortions and complex assembly. Simultaneously, weld formation control technology can keep a well weld formation to adjust the welding procedures after detecting the changes of the seam gap and the width of weld pool, especially for multi-pass welding, air holes would increase in cosmetic welding if the first pass welding has poor formation. So both seam tracking and weld formation control technology cannot only push the development of robot, but significantly benefit for the production.In this paper, the research background is spaceflight manufacturing. The study is based on square-wave AC (alternating current) GTAW (gas tungsten arc welding) with backing bar. The"teach and playback"robot was rebuilt to be a welding robot system with seam tracking and weld formation control.Firstly, the arc welding robot system was constructed, as following,1. The visual sensor with double-layer filter was designed to detect the arc stream in front the weld pool. The computer can capture the clear welding image in different levels of welding current with the sensor device.2. The computer can capture the image by the image capturing card, rectify the position of robot and adjust welding procedures by A/D and D/A cards.3. A multithreading program was developed in Visual C++ computer language to realize the functions of capturing image, image processing, data processing, seam tracking and adjusting welding procedures. A control cycle is 0.2s. In this paper, image features was researched for 6mm thick LD10 aluminum alloy welding during square-wave AC GTAW process. A method of extracting simultaneously arc stream profile and welding seam edge image was proposed. The stable algorithms of image processing was developed including median filter, Roberts operator, automatic threshold value, thinning, fitting edge by least square method, etc. The algorithms can process the images in different light intensity and in different welding current (in the range of 200A~300A), which is usually used for making welds in medium plate aluminum alloy weld (in the range of 3mm~8mm). The precision of image processing is in the range of±(0.1~0.2)mm.A seam tracking technology free from calibrating robot was proposed in order to be easily applied. The visual sensor detects the offset of the torch to the seam center in front of the weld pool, and the computer sends the rectifying voltage to the robot controller in real time.The rectifying principle of robot was analyzed in order to design a sectional type adapting PID controller according to the offset change, which can assure the robot to track seam quickly and stably. The error of seam tracking is in the range of±0.3mm for testing work-piece designed with large offset, and in the range of±0.2mm for usual work-piece.The prediction mode of reinforcement was built for the GTAW welding procedure with backing bar through analyzing the change rule of reinforcement with the change of seam gap, wire feed rate, width of the pool and welding current. The experiment results show the mean error of the mode is 0.085mm and root mean square error is 0.256, so it has high reliability and feasibility.Then, the control technology of backing welding in multi-pass welding was researched. The Fuzzy current controller was built according to detecting seam gap. The closed-loop controller of wire feed rate was constructed using prediction model of reinforcement as the feedback element.The seam tracking technology and weld formation control technology were validated in different shape seam, respectively. The results show that the error of seam tracking is±0.3mm and±0.6mm for line seam and flange seam, respectively, and the length with reinforcement in range of (-0.5, 0) mm can reach 96% and 93% of the whole seam, respectively.At last, the presented arc welding robot system based on passive vision was applied in simulation production of GUABAN and flange production with seam tracking and weld formation technology. The quality of the weldments met the standard of first-order (highest quality according to standardYS010-97) welding seam in terms of dimensions and soundness as demonstrated by x-ray inspection, which fulfilled the requirement of spaceflight productions. |
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