Curiel Braco, DavidSuárez, AlfredoVeiga Suárez, FernandoAldalur, EiderVillanueva Roldán, Pedro2024-11-042024-11-042024-12-01Curiel, D., Suarez, A., Veiga, F., Aldalur, E., Villanueva, P. (2024). Advanced welding automation: Intelligent systems for multipass welding in Butt Double V-Groove and Tee Double Bevel configurations. MethodsX, 13, 1-10. https://doi.org/10.1016/j.mex.2024.103027.2215-016110.1016/j.mex.2024.103027https://academica-e.unavarra.es/handle/2454/52423The paper addresses the imperative shift towards automation in welding processes, leveraging advanced technologies such as industrial robotic systems. Focusing on the reconstruction and classification of weld joints, it introduces a methodology for automatic trajectory determination. Utilizing a laser profilometer mounted on the robot, weld joints are reconstructed in three di- mensions, and spurious data is filtered out through signal processing. A classification algorithm, integrating signal processing and artificial intelligence, accurately categorizes joint profiles, in- cluding V-joints and single bevel T-joints. The proposed intelligent and adaptive system enhances welding automation by analyzing point cloud data from laser scanning to optimize welding tra- jectories. This study establishes a foundational framework for further refinement and broader application in welding automation. Key Points - Introduction of a methodology for automated trajectory determination in welding processes. - Utilization of laser scanning and signal processing for reconstruction and classification of weld joints. - Implementation of an intelligent and adaptive system to optimize welding trajectoriesapplication/pdfeng©2024 The Author(s). This is an open access article under the CC BY-NC-ND license.Robotic weldingPath optimizationThick jointsNeuronal networksSymmetryinfo:eu-repo/semantics/article2024-11-04info:eu-repo/semantics/openAccess