Abstract
In the robotic welding process with thick steel plates, laser vision sensors are widely used to profile the weld seam to implement automatic seam tracking. The weld seam profile extraction (WSPE) result is a crucial step for identifying the feature points of the extracted profile to guide the welding torch in real time. The visual information processing system may collapse when interference data points in the image survive during the phase of feature point identification, which results in low tracking accuracy and poor welding quality. This paper presents a visual attention feature-based method to extract the weld seam profile (WSP) from the strong arc background using clustering results. First, a binary image is obtained through the preprocessing stage. Second, all data points with a gray value 255 are clustered with the nearest neighborhood clustering algorithm. Third, a strategy is developed to discern one cluster belonging to the WSP from the appointed candidate clusters in each loop, and a scheme is proposed to extract the entire WSP using visual continuity. Compared with the previous methods the proposed method in this paper can extract more useful details of the WSP and has better stability in terms of removing the interference data. Considerable WSPE tests with butt joints and T-joints show the anti-interference ability of the proposed method, which contributes to smoothing the welding process and shows its practical value in robotic automated welding with thick steel plates.
Highlights
Robotic automated arc welding processes need different types of sensors to acquire various useful information for welding state monitoring and control of the welding torch, etc. [1, 2]
We presented a great number of methods based on visual attention mechanism to extract weld seam profile (WSP) from strong arc background for butt and fillet joints, such as visual saliency [4, 21] and visual attention models [22]
This paper aims at discerning the clusters belonging to the WSP using the visual attention features with which our eyes can accomplish the task, and struggles to keep more useful details of the WSP for more effective feature identification of the extracted WSP
Summary
Robotic automated arc welding processes need different types of sensors to acquire various useful information for welding state monitoring and control of the welding torch, etc. [1, 2]. [1, 2] Of these sensors, vision sensors are the most widely used [3], and laser vision sensors are commonly employed to detect the weld seam profile (WSP) in robotic thick-steel-plate welding. To implement multipass welding real-time weld seam profile extraction (WSPE) is an indispensable step [4], which makes guiding the welding torch possible using the identified feature points of the extracted WSP. It is true that there are Different joints result in the various appearance of the WSP in the captured image. To extract feature points of V-shaped welding seams, an improved Otsu algorithm and a line detection algorithm were employed by Jawad et al [6]. Fan et al [7] extracted
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