一种用于穿孔塞焊焊缝特征提取的视觉识别算法

针对三代核电站屏蔽厂房钢筋穿孔塞焊变角度、变间隙等复杂的接头特征，研究用于机器人智能化自适应焊接的视觉识别方法. 采用双条纹激光视觉传感技术对焊接前的待焊区域进行识别，结合离散数据模拟关系曲线设计一套视觉处理算法，实现了对各种影响穿孔塞焊接头焊缝成形的特征量的数据进行提取，包括塞焊孔圆心坐标及直径、钢筋凹凸高度、接头倾斜角度、焊缝间隙等，为后续的焊枪姿态与焊接工艺参数联合规划提供基础数据. 通过对比实测的采样数据对该算法的提取精度进行了误差分析. 结果表明，圆心坐标在x, y方向的误差都在 ± 0.2 mm的范围之内，倾斜角度在R_x，R_y方向的误差都在 ± 0.2°的范围之内，实现了穿孔塞焊接头特征提取的高精度和可靠性.

A visual recognition algorithms for seam feature extraction of perforated plug welding

A visual recognition algorithms for seam feature extraction was studied for intelligent adaptive welding of the perforated plug joint between steel-plate and rebar in the shield building of the third generation nuclear power station. In view of the complex characteristics on plug hole size, root gap, inclination angle of perforated plug joint, the double stripe laser vision sensing technology based on the discrete data simulation relation curve was used to identify the area to be welded before welding, The feature datas affecting weld formation were extracted, including the center coordinates and diameter of plug welding hole, the height of rebar-end above steel-plate, the inclination angle of joint, the gap of weld seam, etc., which provide the basic data for the joint planning of welding torch’s gesture and welding process parameters. The error analysis of the extraction accuracy of the algorithm was carried out by comparing the measured sample data. The results show that the center coordinates deviation in the x, y direction are within the range of ± 0.2 mm, the angle deviation in the R_x, R_y direction is within the range of ± 0.2 degree, which realizes the high precision and reliability of feature extraction of perforated plug welding.