Open AccessAn active visual monitoring method for GMAW weld surface defects based on random forest model
Author(s) -
Caixia Zhu,
Haitao Yuan,
Guohong Ma
Publication year - 2022
Publication title -
materials research express
Language(s) - English
Resource type - Journals
ISSN - 2053-1591
DOI - 10.1088/2053-1591/ac5a38
Subject(s) - welding , weld pool , artificial intelligence , gas metal arc welding , process (computing) , decision tree , computer science , feature (linguistics) , noise (video) , computer vision , arc welding , pattern recognition (psychology) , engineering , mechanical engineering , image (mathematics) , gas tungsten arc welding , linguistics , philosophy , operating system
In the automatic manufacturing of robotic welding, real-time monitoring of weld quality is a difficult problem. Meanwhile, due to volatilization of zinc vapor in galvanized steel and complexity of welding process, the existence of welding defects greatly affects industrial production process. And real-time detection of welding defects is a key step in development of intelligent welding. To realize real-time monitoring of weld surface defects, an active visual monitoring method for weld surface defects is proposed. Firstly, after applying Gabor filter to remove interference signals such as arc and noise, obtain weld centerline image; then employ the slope analysis method to extract peak valley coefficient of weld defects, extract five feature points of weld surface quality by Douglas-Puke algorithm, and analyse geometric and spatial distribution features of different types of defects in weld laser stripe images. Finally, using eight feature vectors extracted from weld features, design a weld defect recognition random forest model based on decision tree. After optimizing the decision tree depth and number of model evaluators, compared with the traditional decision tree ID3 and CART algorithm model, this model has better performance than traditional machine learning algorithms on five weld surface defect data sets. The experimental results show that accuracy of weld defect identification in the training set is 99.26%, accuracy of weld defect recognition in the test set is 96%, and processing time of a single image is only 5.3 ms, which overcomes difficulty of real-time weld defect detection in intelligent welding and ensures real-time and accuracy.