A MLMP welding pool classification model for medium-thick low-carbon steel plates based on a VGGNet with a visual attention mechanism

The multi-layer and multi-pass welding (MLMPW) of medium-thick plates in large equipment manufacturing has always been a challenging and important research topic. The core of achieving robotic MLMPW lies in acquiring, monitoring, and classifying the weld pool. To enhance the automation and intelligence of MLMPW, it is necessary to develop an online classification system for weld pool images. A novel MLMPW weld pool classification method is proposed for weld pool images during the welding process, utilizing a VGGNet with a visual attention mechanism (SENet). To improve efficiency and accuracy, pre-trained models from transfer learning are introduced into the network training process. To address the issue of limited MLMPW weld pool data, data augmentation is applied to the dataset. The results indicate that the proposed model can quickly and effectively classify seven types of MLMPW weld pools with an accuracy of 98.39%.