Abstract
In the field of welding robotics, visual sensors, which are mainly composed of a camera and a laser, have proven to be promising devices because of their high precision, good stability, and high safety factor. In real welding environments, there are various kinds of weld joints due to the diversity of the workpieces. The location algorithms for different weld joint types are different, and the welding parameters applied in welding are also different. It is very inefficient to manually change the image processing algorithm and welding parameters according to the weld joint type before each welding task. Therefore, it will greatly improve the efficiency and automation of the welding system if a visual sensor can automatically identify the weld joint before welding. However, there are few studies regarding these problems and the accuracy and applicability of existing methods are not strong. Therefore, a weld joint identification method for visual sensor based on image features and support vector machine (SVM) is proposed in this paper. The deformation of laser around a weld joint is taken as recognition information. Two kinds of features are extracted as feature vectors to enrich the identification information. Subsequently, based on the extracted feature vectors, the optimal SVM model for weld joint type identification is established. A comparative study of proposed and conventional strategies for weld joint identification is carried out via a contrast experiment and a robustness testing experiment. The experimental results show that the identification accuracy rate achieves 98.4%. The validity and robustness of the proposed method are verified.
Highlights
Robotic welding technology is an important indicator on which to measure the technical development of the welding industry in today’s highly developed environment
We compared the proposed feature extraction algorithm for weld images with the weld image feature extraction method presented in reference [23] to verify the effectiveness and superiority of the feature extraction method proposed in this paper
We proposed an algorithm to address the low adaptability and automation of traditional weld joint feature extraction algorithms that are based on visual tracking sensors for traditional weld joint feature extraction algorithms that are based on visual tracking sensors for determining welding parameter configurations in multi weld joint type environments
Summary
Robotic welding technology is an important indicator on which to measure the technical development of the welding industry in today’s highly developed environment. The two most common operating modes for welding robots, namely, the teaching mode and the off-line programming mode, do not depend on sensor measurements during welding; the welding trajectories are set in advance by workers, and the robot moves in accordance with the desired trajectory. These two modes are suitable for use in a standardized, modular, strictly coordinated welding system [1,2,3,4]. In actual welding operations, the welding environment might not be static. An intelligent welding robot uses external sensors to perceive its environment and detect
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