Butt welding joints recognition and location identification by using local thresholding

Shah, Hairol Nizam Mohd; Sulaiman, Marizan; Shukor, Ahmad Zaki; Kamis, Zalina; Rahman, Azrul Azwan Abdul

doi:10.1016/j.rcim.2017.12.007

Cited by 71 publications

(18 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reduced error pruning tree [50], also called the REP-Tree, is characterized by the reduction of the decision tree and the removal of tree subsections that do not improve the classification results (pruning). The splitting criteria employed is the information gain (13). This algorithm was chosen since it allows the tree depth to be controlled.…”

Section: Machine Learning Classificationmentioning

confidence: 99%

“…The actual approaches are either based on passive or active methods. Passive methods use optical cameras, and the bead identification is based on local thresholding over the images [13]. They are affected by illumination conditions and cannot provide any geometrical measure, and require two or more passive cameras to enable a photogrammetric reconstruction.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Weld Bead Detection Based on 3D Geometric Features and Machine Learning Approaches

Rodríguez‐Gonzálvez

Rodríguez‐Martín

2019

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Section: Machine Learning Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Weld Bead Detection Based on 3D Geometric Features and Machine Learning Approaches

Rodríguez‐Gonzálvez

Rodríguez‐Martín

2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…When the last considered points are systematically outliers for several iterations then the edge point is assumed to be the last inlier. Other methods have been proposed to perform weld joint detection and tracking [27,28] Once the edges have been found, the barycenter of the joint is computed according to previous works [18]. Green's theorem is used to compute the position of the barycenter (green in Fig.…”

Section: Laser-guided Navigationmentioning

confidence: 99%

A magnetic climbing robot to perform autonomous welding in the shipbuilding industry

Kermorgant

2018

Robotics and Computer-Integrated Manufacturing

View full text Add to dashboard Cite

In this paper we present the mechanical and control design of a magnetic tracked mobile robot. The robot is designed to move on vertical steel ship hulls and to be able to carry 100 kg payload, including its own weight. The mechanical components are presented and the sizing of the magnetic tracks is detailed. All computation is embedded in order to reduce time delays between processes and to keep the robot functional even in case of signal loss with the ground station. The main sensor of the robot is a 2D laser scanner, that gives information on the hull surface and is used for several tasks. We focus on the welding task and expose the control algorithm that allows the robot to follow a straight line for the welding process.

show abstract

“…However, they also can be applied in industrial context and energy applications [1][2][3][4]. In this way, geomatics techniques can be used to generate three-dimensional models of large and complex scenes as nuclear power plants [5], the generation of as-built models in architecture [6,7] or, even, for metrological tasks related with the quality assessment of products [8][9][10][11]. Portable mobile mapping systems (PMMSs) allow the generation of dense, geo-referenced, three-dimensional models while the operator is moving though the scene [12].…”

Section: Introductionmentioning

confidence: 99%

Validation of Portable Mobile Mapping System for Inspection Tasks in Thermal and Fluid–Mechanical Facilities

et al. 2019

View full text Add to dashboard Cite

The three-dimensional registration of industrial facilities has a great importance for maintenance, inspection, and safety tasks and it is a starting point for new improvements and expansions in the industrial facilities context. In this paper, a comparison between the results obtained using a novel portable mobile mapping system (PMMS) and a static terrestrial laser scanner (TLS), widely used for 3D reconstruction in civil and industrial scenarios, is carried out. This comparison is performed in the context of industrial inspection tasks, specifically in the thermal and fluid-mechanics facilities in a hospital. The comparison addresses the general reconstruction of a machine room, focusing on the quantitative and qualitative analysis of different elements (e.g., valves, regulation systems, burner systems and tanks, etc.). The validation of the PMMS is provided considering the TLS as ground truth and applying a robust statistical analysis. Results come to confirm the suitability of the PMMS to perform inspection tasks in industrial facilities.

show abstract

Butt welding joints recognition and location identification by using local thresholding

Cited by 71 publications

References 10 publications

Weld Bead Detection Based on 3D Geometric Features and Machine Learning Approaches

Weld Bead Detection Based on 3D Geometric Features and Machine Learning Approaches

A magnetic climbing robot to perform autonomous welding in the shipbuilding industry

Validation of Portable Mobile Mapping System for Inspection Tasks in Thermal and Fluid–Mechanical Facilities

Contact Info

Product

Resources

About