ADAP: Adaptive & Dynamic Arc Padding for predicting seam profiles in Multi-Layer-Multi-Pass robotic welding

Abstract

Welding thick metal plates using Multi-Layer-Multi-Pass (MLMP) techniques demands precise control over the weld seam profile as it evolves during the cooling process. In MLMP welding, typically executed with Gas Metal Arc Welding (GMAW) and shielding gas protection, the continuous deposition of weld beads results in dynamic changes to the seam geometry. These challenging traditional robotic welding systems rely on static models. To ensure high-quality joints, real-time adaptation of welding paths requires accurate predictions of weld bead geometry, which in turn guide the estimation of welding positions for adaptive trajectory planning. In this study, we introduce the Adaptive & Dynamic Arc Padding (ADAP) framework. This novel data-driven approach integrates deep learning with an innovative arc-based representation of weld bead profiles. By representing the weld bead geometry through image-derived boundaries and primitive arc parameters (arc center and radius), ADAP establishes a direct link between welding parameters and the evolving weld seam profile. Utilizing datasets generated from Flow-3D simulations of the MLMP process, our framework achieves high-accuracy, real-time predictions: welding positions are estimated within 0.025 s (with an average error of approximately 1.5 mm), and weld seam profiles are predicted in 15 ms, with the arc-based geometric parameters accurately estimated (average errors of 0.73 mm in arc center position and 0.66 mm in radius). This practical approach enhances the efficiency and quality of MLMP robotic welding and contributes to advances in data-driven modeling and intelligent control in manufacturing, paving the way for autonomous welding systems.