Abstract
Abstract
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.
Funder
National Natural Science Foundation of China
Subject
Metals and Alloys,Polymers and Plastics,Surfaces, Coatings and Films,Biomaterials,Electronic, Optical and Magnetic Materials
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献