As an advanced and highly efficient welding method, Keyhole Tungsten Inert Gas (keyhole TIG) welding has drawn wide interests from the manufacturing industry. In order to improve its manufacturing quality and automation level, it’s necessary to develop an online monitoring system for the keyhole TIG welding process. This study developed a visual monitoring system, which utilized an HDR welding camera to monitor the welding pool and keyhole during keyhole TIG welding process. A state of the art Convolutional neural network (Resnet) was developed to recognize different welding states, including good weld, incomplete penetration, burn through, misalignment and undercut. In order to improve the diversity of training dataset, image augmentation was performed. To optimize the training process, a metric learning strategy of center loss was introduced. Furthermore, visualization methods, including guided Grad-CAM, feature map and t-SNE were applied to understand and explain the effectiveness of deep learning process. This study will lay a solid foundation for the development of on-line monitoring system of keyhole TIG.

作为一种先进，高效的焊接方法，匙孔钨极惰性气体保护（Keyhole TIG）焊接引起了制造业的广泛兴趣。为了提高其制造质量和自动化水平，有必要开发一个用于锁孔TIG焊接工艺的在线监控系统。这项研究开发了一种视觉监控系统，该系统使用HDR焊接摄像头在锁孔TIG焊接过程中监视焊池和锁孔。开发了先进的卷积神经网络（Resnet）以识别不同的焊接状态，包括良好的焊接，不完全的熔深，烧穿，错位和咬边。为了提高训练数据集的多样性，进行了图像增强。为了优化训练过程，引入了中心损失的度量学习策略。此外，可视化方法（包括引导Grad-CAM，特征图和t-SNE）被用于理解和解释深度学习过程的有效性。该研究为钥匙孔TIG在线监测系统的开发奠定了坚实的基础。