Next Article in Journal
Stress State Classification Based on Deep Neural Network and Electrodermal Activity Modeling
Next Article in Special Issue
Three-Dimensional Digital Zooming of Integral Imaging under Photon-Starved Conditions
Previous Article in Journal
Supporting Heterogenous Traffic on Top of Point-to-Multipoint Light-Trees
Previous Article in Special Issue
Lensless Three-Dimensional Imaging under Photon-Starved Conditions
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sensors
Volume 23
Issue 5
10.3390/s23052503
sensors-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

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

by
János Hegedűs-Kuti
1,
József Szőlősi
1,
Dániel Varga
1,
János Abonyi
2,
Mátyás Andó
1 and
Tamás Ruppert
2,*
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
*
Author to whom correspondence should be addressed.
Sensors 2023, 23(5), 2503; https://doi.org/10.3390/s23052503
Submission received: 29 December 2022 / Revised: 10 February 2023 / Accepted: 20 February 2023 / Published: 23 February 2023
(This article belongs to the Collection 3D Imaging and Sensing System)
Browse Figures
Review Reports Versions Notes

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.
Keywords: welding; seam defect recognition; DBSCAN; cloud points matching welding; seam defect recognition; DBSCAN; cloud points matching

Share and Cite

MDPI and ACS Style

Hegedűs-Kuti, J.; Szőlősi, J.; Varga, D.; Abonyi, J.; Andó, M.; Ruppert, T. 3D Scanner-Based Identification of Welding Defects—Clustering the Results of Point Cloud Alignment. Sensors 2023, 23, 2503. https://doi.org/10.3390/s23052503

AMA Style

Hegedűs-Kuti J, Szőlősi J, Varga D, Abonyi J, Andó M, Ruppert T. 3D Scanner-Based Identification of Welding Defects—Clustering the Results of Point Cloud Alignment. Sensors. 2023; 23(5):2503. https://doi.org/10.3390/s23052503

Chicago/Turabian Style

Hegedűs-Kuti, János, József Szőlősi, Dániel Varga, János Abonyi, Mátyás Andó, and Tamás Ruppert. 2023. "3D Scanner-Based Identification of Welding Defects—Clustering the Results of Point Cloud Alignment" Sensors 23, no. 5: 2503. https://doi.org/10.3390/s23052503

APA Style

Hegedűs-Kuti, J., Szőlősi, J., Varga, D., Abonyi, J., Andó, M., & Ruppert, T. (2023). 3D Scanner-Based Identification of Welding Defects—Clustering the Results of Point Cloud Alignment. Sensors, 23(5), 2503. https://doi.org/10.3390/s23052503

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop