Weld seam detection is an important part of automated welding.At present,few studies have been conducted on annular weld seams,and a lot of defects exist in the point cloud model of the tube sheet obtained by RGB-D ca...Weld seam detection is an important part of automated welding.At present,few studies have been conducted on annular weld seams,and a lot of defects exist in the point cloud model of the tube sheet obtained by RGB-D cameras and photography methods.Aiming at the above problems,this paper proposed an annular weld seam detection network named WeldNet where a voxel feature encoding layer was adaptively improved for annular weld seams,the sparse convolutional network and region proposal network(RPN)were used to detect annular weld seam position,and an annular weld seam detection loss function was designed.Further,an annular weld seam dataset was established to train the network.Compared with the random sampling consistency(RANSAC)method,WeldNet has a higher detection accuracy,as well as a higher detection success rate which has increased by 23%.Compared with U-Net,WeldNet has been proven to achieve a better detection result,and the intersection over the union of the weld seam detection is improved by 17.8%.展开更多
传统示教焊接方式存在操作过程繁琐,效率低下以及人工依赖度高的问题,为此提出了一种基于Drawing Exchange Format (DXF)文件解析与点云数据处理的无示教自动焊接系统,该系统通过对工件DXF文件进行解析,以获取理想焊缝位置及焊缝类型信...传统示教焊接方式存在操作过程繁琐,效率低下以及人工依赖度高的问题,为此提出了一种基于Drawing Exchange Format (DXF)文件解析与点云数据处理的无示教自动焊接系统,该系统通过对工件DXF文件进行解析,以获取理想焊缝位置及焊缝类型信息,求解多坐标系转换关系实现机器人焊缝初始定位,在此基础上,提出一种考虑机器人实时位姿的激光视觉传感器焊件点云获取方法,进一步开发一种基于平面检测的点云焊缝检测算法,获取实际焊缝位置信息,实现机器人无示教焊接.结果表明,该系统可准确获取焊件点云信息,同时焊缝提取方法误差0.20 mm,满足实际焊接需求.展开更多
基金supported by the Key Research&Development Plan of China(Grant No.2022YFB3404800)the Key Research&Development Plan of Hubei Province(Grant No.2021BAA195)the National Natural Science Foundation of China(Grant No.52188102)。
文摘Weld seam detection is an important part of automated welding.At present,few studies have been conducted on annular weld seams,and a lot of defects exist in the point cloud model of the tube sheet obtained by RGB-D cameras and photography methods.Aiming at the above problems,this paper proposed an annular weld seam detection network named WeldNet where a voxel feature encoding layer was adaptively improved for annular weld seams,the sparse convolutional network and region proposal network(RPN)were used to detect annular weld seam position,and an annular weld seam detection loss function was designed.Further,an annular weld seam dataset was established to train the network.Compared with the random sampling consistency(RANSAC)method,WeldNet has a higher detection accuracy,as well as a higher detection success rate which has increased by 23%.Compared with U-Net,WeldNet has been proven to achieve a better detection result,and the intersection over the union of the weld seam detection is improved by 17.8%.
文摘传统示教焊接方式存在操作过程繁琐,效率低下以及人工依赖度高的问题,为此提出了一种基于Drawing Exchange Format (DXF)文件解析与点云数据处理的无示教自动焊接系统,该系统通过对工件DXF文件进行解析,以获取理想焊缝位置及焊缝类型信息,求解多坐标系转换关系实现机器人焊缝初始定位,在此基础上,提出一种考虑机器人实时位姿的激光视觉传感器焊件点云获取方法,进一步开发一种基于平面检测的点云焊缝检测算法,获取实际焊缝位置信息,实现机器人无示教焊接.结果表明,该系统可准确获取焊件点云信息,同时焊缝提取方法误差0.20 mm,满足实际焊接需求.
