Advanced welding automation: Intelligent systems for multipass welding in Butt Double V-Groove and Tee Double Bevel configurations
| dc.contributor.author | Curiel Braco, David | |
| dc.contributor.author | Suárez, Alfredo | |
| dc.contributor.author | Veiga Suárez, Fernando | |
| dc.contributor.author | Aldalur, Eider | |
| dc.contributor.author | Villanueva Roldán, Pedro | |
| dc.contributor.department | Ingeniería | es_ES |
| dc.contributor.department | Ingeniaritza | eu |
| dc.date.accessioned | 2024-11-04T08:40:47Z | |
| dc.date.available | 2024-11-04T08:40:47Z | |
| dc.date.issued | 2024-12-01 | |
| dc.date.updated | 2024-11-04T08:30:10Z | |
| dc.description.abstract | The 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 trajectories | en |
| dc.description.sponsorship | The authors acknowledge the Basque Government for financing the H2OCEAN project, HAZITEK 2021 program [ZE-2021/00037] and HYSHORE project HAZITEK 2021 program [ZE-2021/00032]. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Curiel, 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. | |
| dc.identifier.doi | 10.1016/j.mex.2024.103027 | |
| dc.identifier.issn | 2215-0161 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/52423 | |
| dc.language.iso | eng | |
| dc.publisher | Elsevier | |
| dc.relation.ispartof | MethodsX (2024), vol. 13, 103027 | |
| dc.relation.publisherversion | https://doi.org/10.1016/j.mex.2024.103027 | |
| dc.rights | ©2024 The Author(s). This is an open access article under the CC BY-NC-ND license. | |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Robotic welding | en |
| dc.subject | Path optimization | en |
| dc.subject | Thick joints | en |
| dc.subject | Neuronal networks | en |
| dc.subject | Symmetry | en |
| dc.title | Advanced welding automation: Intelligent systems for multipass welding in Butt Double V-Groove and Tee Double Bevel configurations | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/publishedVersion | |
| dspace.entity.type | Publication | |
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