大型压力容器内部焊接点位实时检测方法  

Real-Time Detection Method for Welding Points Inside Large Pressure Vessels

作　　者：宋丽梅[1] 张祺乐 王烁鹏 陈恩泽 杨燕罡[2] 朱新军[1] SONG Limei;ZHANG Qie;WANG Shuopeng;CHEN Enze;YANG Yangang;ZHU Xinjun(Tianjin Key Laboratory of Intelligent Control of Electrical Equipment,Tiangong University,Tianjin 300387,China;Tianjin University of Technology and Education,Tianjin 300222,China)

机构地区：[1]天津工业大学天津市电气装备智能控制重点实验室,天津300387 [2]天津职业技术师范大学,天津300222

出　　处：《航空制造技术》2023年第7期36-42,49,共8页Aeronautical Manufacturing Technology

基　　金：国家自然科学基金(61905178);天津市科学技术普及项目(22KPXMRC00090)。

摘　　要：为了解决大型压力容器铝合金罐体内部防浪板自动化焊接过程中,机器人系统对焊接点位检测不智能、检测精度不高、检测鲁棒性差的问题,设计了一种结构光视觉引导机器人的多特征融合三维焊接点位实时检测方法。首先对工件进行视觉特征提取,得出多个感兴趣区域(Region of interest,ROI),然后将多个特征融合,得到二维关键点,最后通过快速、无约束的系统标定确定三维预焊接点位。工业现场试验得出,相机坐标系下三维焊接点位提取最大误差为0.196 mm,平均误差0.099 mm,平均检测时间0.09 s,焊接点位检测精准、快速、智能,能够满足工业机器人焊接路径规划及自动焊接任务。In order to solve the problems of unintelligent detection of weld points,low detection accuracy and poor detection robustness of the robot system in the automated welding process of the internal anti-surge plate of aluminum alloy tanks of large pressure vessels,a multi-feature fusion 3D weld point real-time detection method for structural light vision-guided robots is designed.First,the visual features are extracted from the workpiece to derive multiple regions of interest,then the multiple features are fused to obtain the 2D key points,and finally the 3D pre-weld points are determined by fast and unconstrained system calibration.Industrialfield experiments show that the maximum error of 3D welding point extraction in camera coordinate system is 0.196 mm,the average error is 0.099 mm,and the average detection time is 0.09 s.The welding point detection is accurate,fast and intelligent,which can meet the industrial robot welding path planning and automatic welding tasks.

关 键 词：计算机视觉 三维重建 机器人焊接 防浪板焊接 深度学习 

分 类 号：TG409[金属学及工艺—焊接] TP391.41[自动化与计算机技术—计算机应用技术]