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A passive vision sensor is added to a teaching and playback welding robot for pulsed gas tungsten arc welding, which is normally used in the welding of thin plate closed-gap butts. This paper presents a seam tracking method based on this sensor through period visual measurement of the offset between the torch and the seam center. A robust image processing algorithm is developed to extract the seam center. A kind of ARX(auto-regressive with exogenous input) model is studied to describe the relationship between the rectifying voltage and the offset. The fuzzy PID(proportional-integral-derivative) seam tracking controller is analyzed and designed in consideration of various offsets based on the ARX model. The experimental results on straight line weld display a good seam tracking capability of the proposed method. [ABSTRACT FROM AUTHOR]

The automatic control in square-wave alternating current (AC) gas tungsten arc welding (GTAW) is significant for a “teach and playback” robot to overcome the variation of the seam trajectory and the seam gap in the welding process. This paper presents a welding robot system based on the real-time visual measurement in the different levels of the welding current. The primary objective is to measure the offset of the torch to the seam center and the size of seam gap by passive vision, track the seam and control the weld pool in real time. A novel visual image analysis algorithm was developed for seam tracking and seam gap measuring, free from robot calibration. The control algorithm based on the knowledge base was established to control the weld formation by regulating the welding current and wire feed rate. The welding practice for the rocket storage tank demonstrates the efficiency of the proposed system. [ABSTRACT FROM AUTHOR]

The autonomous localization of initial weld position is one of the key technologies to realize intellectualized welding. This paper presented a practical system and method to guide the welding robot to the initial position of the weld seam. Using template matching and polynomial interpolation technology of pixel, the position of initial weld position is located at the sub-pixel level in the image plane. Simple and practical calibration technology avoids the using of complicated algorithms and costly measuring apparatus. Aluminium and its alloys reflect the light strongly, which make the recognition of the weld seam more difficult than steel. Taking the butt joint weld seam of a curve aluminium alloy sheet as an example, the autonomous localization function is realized accurately. For those instances with bigger errors, we advanced a method to rectify the deviation. The welding robot can be guided to the initial position of planar weld seam and meets the requirements to execute welding operation directly. The whole procedure has the characters of simple, practical, and strong anti-jamming. [ABSTRACT FROM AUTHOR]

The weld bead shape is crucial for weld quality. Automatic inspection on the 3D shape of a weld bead is achieved by recording the image of the weld bead with an optical sensing method and then calculating the 3D shape parameters with a shape-from-shading technology based on a single image in the computer-vision domain. The paper first introduces a method of shape from shading based on a single image and puts forward its implementation. It then presents the improved measures of the original algorithm according to the defects of the computed results and finds accuracy compared with the former method by a synthetic image. Finally, the improved algorithm is applied to the surface height reconstruction of weld bead successfully . [ABSTRACT FROM AUTHOR]

In manual welding, an experienced welder acquires 80% of the welding process information by vision. Visual sensing has the advantage of supplying abundant information without contact with the workpiece or welding circuit. Visual sensors have been widely used in the field of welding. According to optical imaging principles, the object imaging process depends on the shape, size, position, and angle of the light source; the shape of the object; the reflectance characteristic of the object’s surface; and the position, angle, and inner parameters of the camera. In order better to utilize the image information of a visual sensor and to ascertain the weld shape and size, arc light intensity is calculated from the arc length and the welding current. The visual sensing system is analyzed from the point of the view of light intensity. The function of adjusting the system to the light intensity is formulated in this paper. A relationship between the gray image, arc length, and welding current can be obtained. Welding experiments were implemented. Thus, we can recover the shape and height of the weld pool by SFS (shape from shading) algorithms from the weld pool image. [ABSTRACT FROM AUTHOR]