The ability to predict the geometry of the weld bead plays a key role in accurate path planning and determination of welding parameters in wire arc additive manufacturing. However, little attention has been paid to the weld bead geometry and its prediction when the deposition path is not straight. Thus, this work focuses on the 3D reconstruction of the weld bead based on the deposition path. One of the main findings of this paper is that the weld bead shape changes from a symmetrical cross-section in straight portions of the path to an asymmetrical shape in non-straight regions. To predict the 3D geometry of the weld bead, an AI-based architecture called AIBead was developed. A suitable parametrization of the deposition path is proposed that is a key to train the AIBead properly and to outperform currently used parabolic models.

预测焊道几何形状的能力在电弧增材制造中精确路径规划和确定焊接参数方面起着关键作用。然而，当熔敷路径不直时，很少关注焊道几何形状及其预测。因此，这项工作的重点是基于沉积路径的焊道 3D 重建。本文的主要发现之一是焊道形状从路径直线部分的对称横截面变为非直线区域的不对称形状。为了预测焊缝的 3D 几何形状，开发了一种名为 AIBead 的基于人工智能的架构。建议对沉积路径进行合适的参数化，这是正确训练 AIBead 并优于当前使用的抛物线模型的关键。