3D Scanner-Based Identification of Welding Defects-Clustering the Results of Point Cloud Alignment.

János Hegedűs-Kuti, József Szőlősi, Dániel Varga, János Abonyi, Mátyás Andó, Tamás Ruppert

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Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

János Hegedűs-KutiFaculty of Informatics, Savaria Institute of Technology, Eotvos Lorand University, H-9700 Szombathely, Hungary.ORCID 0000-0002-1400-7540

József SzőlősiFaculty of Informatics, Savaria Institute of Technology, Eotvos Lorand University, H-9700 Szombathely, Hungary.ORCID 0000-0003-1808-7071

Dániel VargaFaculty of Informatics, Savaria Institute of Technology, Eotvos Lorand University, H-9700 Szombathely, Hungary.

János AbonyiELKH-PE Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, H-8200 Veszprem, Hungary.ORCID 0000-0001-8593-1493

Mátyás AndóFaculty of Informatics, Savaria Institute of Technology, Eotvos Lorand University, H-9700 Szombathely, Hungary.ORCID 0000-0002-3848-9505

Tamás RuppertELKH-PE Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, H-8200 Veszprem, Hungary.ORCID 0000-0001-9441-843X

Funding

National Research, Development and Innovation Office OTKA 143482National Research, Development and Innovation Office TKP2021-NVA-10

6 · The paper itself

Abstract

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.

Indexed as

cloud points matchingDBSCANseam defect recognitionwelding

Identifiers

PMID36904704

DOI10.3390/s23052503

PMCPMC10007542

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