Faculty of Informatics, Savaria Institute of Technology, Eotvos Lorand University, H-9700 Szombathely, Hungary.
ELKH-PE Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, H-8200 Veszprem, Hungary.
Sensors (Basel). 2023 Feb 23;23(5):2503. doi: 10.3390/s23052503.
This paper describes a framework for detecting welding errors using 3D scanner data. The proposed approach employs density-based clustering to compare point clouds and identify deviations. The discovered clusters are then classified according to standard welding fault classes. Six welding deviations defined in the ISO 5817:2014 standard were evaluated. All defects were represented through CAD models, and the method was able to detect five of these deviations. The results demonstrate that the errors can be effectively identified and grouped according to the location of the different points in the error clusters. However, the method cannot separate crack-related defects as a distinct cluster.
本文描述了一种使用 3D 扫描仪数据检测焊接缺陷的框架。该方法采用基于密度的聚类来比较点云并识别偏差。然后根据标准焊接故障类别对发现的聚类进行分类。评估了 ISO 5817:2014 标准中定义的六个焊接偏差。所有缺陷都通过 CAD 模型表示，该方法能够检测到其中的五个偏差。结果表明，可以根据误差聚类中不同点的位置有效地识别和分组误差。但是，该方法无法将与裂缝相关的缺陷作为一个单独的聚类分离出来。
