AF-FTTSnet: An end-to-end two-stream convolutional neural network for online quality monitoring of robotic welding

Abstract

Online welding quality monitoring (WQM) is crucial for intelligent welding, and deep learning approaches considering spatiotemporal features for WQM tasks show great potential. However, one of the important challenges for existing approaches is to balance the spatiotemporal representation learning capability and computational efficiency, which makes it challenging to adapt welding processes with complex and drastic molten pool dynamic behavior. This paper proposes a novel approach for WQM using molten pool visual sensing and deep learning considering spatiotemporal features, the proposed deep learning network called attention fusion based frame-temporality two-stream network (AF-FTTSnet). Firstly, a passive vision sensor is used to acquire continuous dynamic molten pool images. Meanwhile, temporal difference images are computed to provide novel features and temporal representations. Then, a two-stream feature extraction module is designed to concurrently extract rich spatiotemporal features from molten pool images and temporal difference images. Finally, an attention fusion module with the ability to automatically identify and weight the most relevant features is designed to achieve optimal fusion of the two-stream features. The shop welding experimental results indicate that the proposed AF-FTTSnet model can effectively and robustly recognize five typical welding states during helium arc welding, with an accuracy of 99.26%. This model has been demonstrated to exhibit significant performance improvements compared to mainstream temporal sequence models. Available: https://github.com/Just199806/TSCNN/tree/master.