Online identification of burn-through and weld deviation in sheet lap MIG welding based on YOLOv5

Online monitoring of the welding process is very important to the development of the modern manufacturing industry. However, monitoring welding defects in real-time and with high accuracy is challenging because welding is inherently a dynamic, non-linear process. In this paper, the forming defects generated during the melt Inert-gas welding of sheet steel as the object of research. We propose a multi-task simultaneous monitoring system for welding defects based on the YOLOv5 model, which can achieve highly accurate simultaneous detection of burn-through and weld deviation defects. Firstly, a passive vision system has been designed that can filter out strong arc light interference. In combination with a self-made camera bracket with flexible adjustment and repeatable positioning, clear images of the molten pool can be acquired. Compared to common methods that require a laser for weld deviation detection, all monitoring tasks can be performed with just one charge coupled device camera, significantly reducing the cost of deploying the system. Secondly, to obtain experimental data closer to the industrial field environment. We simulated different degrees of local and continuous burn-through by changing three parameters: welding current, plate thickness, and lap width. Four different deviations: left, right, and tilt were designed to obtain rich weld deviation image data. Finally, based on the above data, we compared several aspects such as detection speed, recognition accuracy, and loss function. The YOLOv5s model with the smallest model parameters was finally selected as the base model. The results show that our proposed multi-task simultaneous monitoring system achieves an average accuracy of 98.84% for the identification of four molten pool states. Burn-through defect and weld deviation detection with more than 50 frames per second can be achieved in the online detection state. This paper can provide some guidance for the online monitoring of the welding manufacturing process.