This work aims at a novel approach to estimate the root-pass penetration towards its feedback control, in which the real penetration is measured by the backside bead width. The major challenge is that it happens under the workpiece and likely cannot be directly observable. The dynamic evolution of the weld pool surface has been analysed to design an active vision method monitoring the pool surface, yet fundamentally correlated to the unobservable penetration. The designed convolutional neural network model is trained, validated, and tested for recognising the weld penetration with satisfactory accuracy.

中文翻译：

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通过焊缝池图像的深度学习动态估计接头的穿透力

这项工作旨在通过一种新颖的方法来估计根通量对其反馈控制的穿透力，其中，真实的穿透力是通过背面磁珠宽度来测量的。主要的挑战是它发生在工件下方，可能无法直接观察到。分析了熔池表面的动态演变，以设计一种主动的视觉方法来监视熔池表面，但从根本上与不可观察的熔深相关。对设计的卷积神经网络模型进行了训练，验证和测试，以令人满意的精度识别焊缝熔深。