An active visual monitoring method for GMAW weld surface defects based on random forest model

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.

GAN-Based Image Dehazing for Intelligent Weld Shape Classification and Tracing Using Deep Learning

“…It also helps to identify the different sizes and scales for the same object. Finally, the head is same as the YOLOv3 and v4 versions, and is used to perform the detection with vectors of class probabilities, objectness scores, and bounding boxes [43]. The YOLOv5 architecture is shown in Figure 6.…”

CRGF-YOLO: An Optimized Multi-Scale Feature Fusion Model Based on YOLOv5 for Detection of Steel Surface Defects

“…Fourier transform [4], Gabor filter [5], SVM (Support Vector Machine) [6] and Random Forest [7]. However, there are limitations of algorithms above: the artificial feature extraction cannot express information adequately under complex circumstances, defect performance is highly affected by human-generated feature extractors, problems like huge computations, locations and sizes of defects are still difficult to solve.…”

Intelligent Metal Welding Defect Detection Model on Improved FAST-PNN

“…With the wide application of intelligent welding defect detection instruments in engineering practice, how to accurately identify welding defects under complex constraints has become a research hotspot, and the existing research results demonstrate that the performance of welding defect recognition models based on artificial neural networks is particularly outstanding [10][11][12][13]. Welding defect recognition based on neural networks [14,15] involves selecting the parameters sensitive to defects as the input of the neural network, training the neural network through the defect data in the numerical simulation and finally applying the trained network to the detection of metal parts to realize the automatic identification of defects [16][17][18][19][20]. Ma et al constructed a Convolutional Neural Network (CNN) to identify the spectrum graphs, realizing the online detection of porosity [21].…”