3D Scanner-Based Identification of Welding Defects—Clustering the Results of Point Cloud Alignment-Reference-Cited by-同舟云学术

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

Published:2023-02-23 Issue:5 Volume:23 Page:2503
ISSN:1424-8220
Container-title:Sensors
language:en
Short-container-title:Sensors

Author:

Hegedűs-Kuti János¹^ORCID,Szőlősi József¹^ORCID,Varga Dániel¹,Abonyi János²^ORCID,Andó Mátyás¹^ORCID,Ruppert Tamás²^ORCID

Affiliation:

1. Faculty of Informatics, Savaria Institute of Technology, Eotvos Lorand University, H-9700 Szombathely, Hungary

2. ELKH-PE Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, H-8200 Veszprem, Hungary

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.

Funder

Monitoring Complex Systems

Ministry for Innovation and Technology of Hungary

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Link

https://www.mdpi.com/1424-8220/23/5/2503/pdf

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