Yun Shi and Yanyan Zhu


Visual recognition system, machine learning, welding robot, image preprocessing, median filtering


As an important automation equipment, welding robot has been widely used in the production process of various industries. In the welding process, due to the influence of welding parameters, technology and other factors, the shape, size, and quality of the weld would be different. Traditional methods usually use hand-designed feature extraction algorithms, which need to rely on the experience of domain experts, which limits the accurate extraction of key features, such as welds. The machine learning model is adopted to learn features from a large number of welding image data, which avoids the manually designed feature extraction algorithm and improves the ability of accurately extracting weld features. Developing a system that can automatically extract and identify key welding features is of great significance for improving the automation level and work efficiency of welding robots. In this paper, the data set containing weld defect images was selected for experiment, and the median filter was used to process the original images. Using the method of machine learning, a welding robot visual recognition system based on machine learning was designed. By comparing with rule-based visual recognition system, this paper tested its performance. According to the experimental results, when the training set size was 600, the accuracy rate of the rules-based visual recognition system was 0.88, while the accuracy rate of the machine learning system was 0.95. This shows that the system built in this paper has better performance. The system in this paper can improve welding quality and efficiency, realise automation and intelligent production, cope with complex environment and change, and promote the development of intelligent manufacturing. This would promote the innovation and progress of the welding process, and provide technical support and impetus for the upgrading and transformation of the manufacturing industry.

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