Advances in Manufacturing ( IF 5.2 ) Pub Date : 2020-11-10 , DOI: 10.1007/s40436-020-00325-y Ying-Zhong Tian , Hong-Fei Liu , Long Li , Wen-Bin Wang , Jie-Cai Feng , Feng-Feng Xi , Guang-Jie Yuan
For welding path determination, the use of vision sensors is more effective compared with complex offline programming and teaching in small to medium volume production. However, interference factors such as scratches and stains on the surface of the workpiece may affect the extraction of weld information. In the obtained weld image, the weld seams have two distinct features related to the workpiece, which are continuous in a single process and separated from the workpiece’s gray value. In this paper, a novel method is proposed to identify the welding path based on the region of interest (ROI) operation, which is concentrated around the weld seam to reduce the interference of external noise. To complete the identification of the entire welding path, a novel algorithm is used to adaptively generate a dynamic ROI (DROI) and perform iterative operations. The identification accuracy of this algorithm is improved by setting the boundary conditions within the ROI. Moreover, the experimental results confirm that the coefficient factor used for determining the ROI size is a pivotal influencing factor for the robustness of the algorithm and for obtaining an optimal solution. With this algorithm, the welding path identification accuracy is within 2 pixels for three common butt weld types.
中文翻译:
基于感兴趣区域操作的焊缝鲁棒识别
对于焊接路径的确定,与复杂的离线编程和中小批量生产的教学相比,视觉传感器的使用更为有效。但是,工件表面上的划痕和污点等干扰因素可能会影响焊接信息的提取。在获得的焊缝图像中,焊缝具有与工件相关的两个不同特征,它们在单个过程中是连续的,并且与工件的灰度值分开。本文提出了一种基于感兴趣区域(ROI)操作来识别焊接路径的新方法,该方法集中在焊缝周围以减少外部噪声的干扰。为了完成整个焊接路径的识别,一种新颖的算法用于自适应地生成动态ROI(DROI)并执行迭代操作。通过在ROI中设置边界条件,可以提高该算法的识别精度。此外,实验结果证实,用于确定ROI大小的系数因子是算法稳健性和获得最佳解决方案的关键影响因素。使用此算法,对于三种常见的对接焊缝类型,焊接路径识别精度在2个像素以内。实验结果证实,用于确定ROI大小的系数因子是算法稳健性和获得最佳解决方案的关键影响因素。使用此算法,对于三种常见的对接焊缝类型,焊接路径识别精度在2个像素以内。实验结果证实,用于确定ROI大小的系数因子是算法稳健性和获得最佳解决方案的关键影响因素。使用此算法,对于三种常见的对接焊缝类型,焊接路径识别精度在2个像素以内。