In a single bevel GMAW (gas metal arc welding) with gap fluctuation, a deep learning model was constructed using the monitoring image during the welding to predict the welding quality. We utilized Python and the library Keras and created a CNN (Convolutional neural network) model using the top surface image including the molten pool as an input. The classification model was used to predict the burn-through, and the regression model was used to estimate the penetration depth. As a result, the excessive penetration and burn-through could be predicted in advance and more than 95 % of estimated results of penetration depth were less 1 mm error for stepped and tapered sample shapes.

在具有间隙波动的单斜角GMAW（气体金属电弧焊）中，使用焊接期间的监控图像构建深度学习模型，以预测焊接质量。我们使用Python和库拉斯（Keras）库，并使用包括熔池作为输入的顶表面图像创建了CNN（卷积神经网络）模型。使用分类模型预测烧穿，使用回归模型估计渗透深度。结果，可以预先预测过度的穿透和烧穿，并且对于阶梯状和锥形样品形状，超过95％的穿透深度估计结果的误差小于1 mm。