RT info:eu-repo/semantics/article
T1 Weld bead detection based on 3D geometric features and machine learning approaches
A1 Rodríguez Gonzálvez, Pablo
A1 Rodríguez-Martín, Manuel
A2 Ingeniería CartograficaGeodesica y Fotogrametria
K1 Ingenierías
K1 Welding
K1 Machine learning
K1 Decision tree
K1 Weld bead
K1 Photogrammetry
K1 3D model
K1 Non-destructive testing
AB Weld bead detection is essential for automated welding inspection processes. The non-invasive passive techniques, such as photogrammetry, are quickly evolving to provide a 3D point cloud with submillimeter precision and spatial resolution. However, its application in weld visual inspection has not been extensively studied. The derived 3D point clouds, despite the lack of topological information, store significant information for the weld-plaque segmentation. Although the weld bead detection is being carried out over images or based on laser profiles, its characterization by means of 3D geometrical features has not been assessed. Moreover, it is possible to combine machine learning approaches and the 3D features in order to realize the full potential of the weld bead segmentation of 3D submillimeter point clouds. In this paper, the novelty is focused on the study of 3D features on real cases to identify the most relevant ones for weld bead detection on the basis of the information gain. For this novel contribution, the influence of neighborhood size for covariance matrix computation, decision tree algorithms, and split criteria are analyzed to assess the optimal results. The classification accuracy is evaluated by the degree of agreement of the classified data by the kappa index and the area under the receiver operating characteristic (ROC) curve. The experimental results show that the proposed novel methodology performs better than 0.85 for the kappa index and better than 0.95 for ROC area.
PB IEEE
SN 2169-3536
LK http://hdl.handle.net/10612/12349
UL http://hdl.handle.net/10612/12349
NO 14 p.
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 27-abr-2024