Monitoring of weld defects of visual sensing assisted GMAW process with galvanized steel
Guohong Ma, Haitao Yuan, Lesheng Yu, Yinshui He
Abstract
Due to the existence of high-temperature and high-pressure zinc vapor in the gas metal arc welding (GMAW) process of galvanized steel, it is difficult to achieve accurate and real-time detection of weld defects, which brings great challenges to robotic welding manufacturing. In order to realize the automatic classification and prediction of weld defects, a method of galvanized steel weld defects detection based on active visual sensing and machine learning was proposed. First, Gabor filter was used to remove arc light, noise, and other interference signals to obtain the weld centerline image. Then, according to the five different weld defects of the geometric and spatial distribution characteristics in weld centerline image were analyzed with the principle of sub-pixel level. Finally, using the eight feature parameters extracted from the weld feature points, a variable learning rate and momentum factor back propagation (VL-MFBP) neural network model was designed. The model introduced a variable learning rate and momentum factor to quickly find the optimal solution in a short time, its performance was better than traditional machine learning algorithms. The experimental results show that the accuracy of weld defect recognition is 98.15%, and the average processing time of a single image is only 183.74 ms.