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作 者:牛子明 熊凌[1,2] 胡晓东 刘晨曦 李澳 信石玉 陈刚 但斌斌 Niu Ziming;Xiong Ling;Hu Xiaodong;Liu Chenxi;Li Ao;Xin shiyu;Chen Gang;Dan Binbin(School of Information Science and Engineering,Wuhan University of Science and Technology,Wuhan 430081;Institute of Robotics and Intelligent Systems,Wuhan University of Science and Technology,Wuhan 430081;Wuhan Hanguang Steel Products Construction Engineering Co.,Ltd.,Wuhan 430081;Kingdream Public Limited Company of Sinopecoil Field Equipment Corporation,Wuhan 430223;Hubei Open University,Wuhan 430074;Key Laboratory of Metallurgical Equipment and Control Technology,Wuhan University of Science and Technology,Wuhan 430081)
机构地区:[1]武汉科技大学信息科学与工程学院,湖北武汉430081 [2]武汉科技大学机器人与智能系统研究院,湖北武汉430081 [3]武汉汉光钢品建设工程有限公司,湖北武汉430081 [4]中石化江钻石油机械有限公司,湖北武汉430223 [5]湖北开放大学,湖北武汉430074 [6]武汉科技大学冶金装备及其控制教育部重点实验室,湖北武汉430081
出 处:《冶金设备》2025年第1期1-7,共7页Metallurgical Equipment
基 金:武汉市重点研发计划项目(2023010402010603);武汉市重点研发计划项目(2024010702030103)。
摘 要:本研究针对大型钢结构制造中焊缝形状复杂、人工识别效率低、焊接路径优化困难等问题,提出了一种基于激光雷达点云的智能焊接方法。该方法通过激光雷达获取三维点云数据,结合随机样本一致性算法进行点云分割和焊缝特征提取,并利用改进的遗传算法优化焊接轨迹。实验结果表明,该方法在焊缝识别准确性和路径优化效果方面具有显著优势,为大型钢结构的智能焊接提供了一种有效的解决方案。This study addresses the challenges of complex weld seam features,low efficiency in manual recognition,and difficulties in welding path optimization during the manufacturing of large steel structures.An intelligent welding method based on LiDAR point cloud data is proposed.The method integrates LiDAR scanning to acquire high-precision three-dimensional point cloud data,applies the Random Sample Consensus(RANSAC)algorithm for point cloud segmentation and weld seam feature extraction,and employs an improved genetic algorithm to optimize welding trajectories.Experimental results demonstrate significant advantages of this method in weld seam recognition accuracy and path optimization effectiveness,providing a reliable solution for intelligent welding of large steel structures.
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