Curiel David, Suárez Alfredo, Veiga Fernando, Aldalur Eider, Villanueva Pedro
Engineering Department, Public University of Navarra, Los Pinos Building, Arrosadía Campus, Pamplona, Navarra E31006, Spain.
TECNALIA, Basque Research and Technology Alliance (BRTA), Science and Technology Park of Gipuzkoa, Donostia-San Sebastián E20009, Spain.
MethodsX. 2024 Oct 28;13:103027. doi: 10.1016/j.mex.2024.103027. eCollection 2024 Dec.
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 dimensions, and spurious data is filtered out through signal processing. A classification algorithm, integrating signal processing and artificial intelligence, accurately categorizes joint profiles, including 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 trajectories. 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.
本文论述了焊接工艺向自动化的迫切转变,利用了工业机器人系统等先进技术。聚焦于焊接接头的重建和分类,介绍了一种自动轨迹确定方法。利用安装在机器人上的激光轮廓仪,对焊接接头进行三维重建,并通过信号处理滤除虚假数据。一种集成信号处理和人工智能的分类算法,能准确地对接头轮廓进行分类,包括V型接头和单斜边T型接头。所提出的智能自适应系统通过分析激光扫描的点云数据来优化焊接轨迹,从而提高焊接自动化程度。本研究为焊接自动化的进一步完善和更广泛应用建立了基础框架。要点• 介绍焊接工艺中自动轨迹确定方法。• 利用激光扫描和信号处理进行焊接接头的重建和分类。• 实施智能自适应系统以优化焊接轨迹。