Online penetration monitoring of complex grooves remains challenging due to steel plates’ groove instability and welding heat distortion. Penetration is an accumulation process of material deposition. Temporal signals, such as video, can provide a more comprehensive characterization of the melt pool state. A deep learning method based on continuous video is designed to monitor groove welding penetration in-process. The proposed Fast Video-feature Extraction Net (FVENet) consists of a video extraction module and a multi-feature screening module. The efficient network can quickly extract high-dimensional data features in complex arc environments and achieve accurate results for backside melt width prediction. The feature extraction process of the network is explored by visualizing the results of different network layers. Experimental results indicate that the mean squared error (MSE) of FVENet reaches 0.0634 mm, outperforming other mainstream deep learning frameworks. The inference time under video input reaches 100 FPS. The network structure designed in this paper has the potential to become a universal template for processing melt pool images.

由于钢板坡口不稳定和焊接热变形，复杂坡口的在线熔深监测仍然具有挑战性。渗透是物质沉积的积累过程。视频等时间信号可以提供熔池状态的更全面的表征。设计了一种基于连续视频的深度学习方法来监测坡口焊接熔深。所提出的快速视频特征提取网络（FVENet）由视频提取模块和多特征筛选模块组成。高效的网络可以快速提取复杂电弧环境下的高维数据特征，并获得准确的背面熔宽预测结果。通过可视化不同网络层的结果来探索网络的特征提取过程。实验结果表明，FVENet的均方误差（MSE）达到0.0634 mm，优于其他主流深度学习框架。视频输入下的推理时间达到100 FPS。本文设计的网络结构有潜力成为处理熔池图像的通用模板。