PSI - Issue 75

Available online at www.sciencedirect.com Available online at www.sciencedirect.com Available online at www.sciencedirect.com

ScienceDirect

Procedia Structural Integrity 75 (2025) 120–128 Structural Integrity Procedia 00 (2025) 000–000 Structural Integrity Procedia 00 (2025) 000–000

www.elsevier.com / locate / procedia www.elsevier.com / locate / procedia

© 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under the responsibility of Dr Fabien Lefebvre with at least 2 reviewers per paper Current studies have shown that the detection of the weld seam is the major challenge for weld quality assessment based on local geometrical parameters, such as toe angle and notch radii. In this paper, di ff erent algorithms are presented that enable an automated weld seam detection. Three approaches are investigated: algorithm-based methods that are based on curvature of the surface, methods based on the random sample consensus (RANSAC) algorithm or artificial neural networks (ANN). All methods are presented in detail and applied to a di ff erent weld geometries. © 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http: // creativecommons.org / licenses / by-nc-nd / 4.0 / ) Peer-review under responsibility of the scientific committee of the Fatigue Design 2025 organizers. Keywords: Point cloud processing; weld detection; weld quality; fatigue strength Abstract 3D laser scans are increasingly used for the analysis of the weld quality. Based on these scans, it is possible to determine the weld profiles relevant for the fatigue strength, i.e. the weld angle and notch radii or other features such as spatter or undercuts. These methods are currently being developed and demonstrated in scientific investigations. However, in order to enable the applicability of 3D laser scans for quality assessment in practice, methods are still needed to automatically and reliably identify the weld seam and the weld direction from 3D scans. With this information, the weld profile in 2D cross-sections can be evaluated. Current studies have shown that the detection of the weld seam is the major challenge for weld quality assessment based on local geometrical parameters, such as toe angle and notch radii. In this paper, di ff erent algorithms are presented that enable an automated weld seam detection. Three approaches are investigated: algorithm-based methods that are based on curvature of the surface, methods based on the random sample consensus (RANSAC) algorithm or artificial neural networks (ANN). All methods are presented in detail and applied to a di ff erent weld geometries. © 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http: // creativecommons.org / licenses / by-nc-nd / 4.0 / ) Peer-review under responsibility of the scientific committee of the Fatigue Design 2025 organizers. Keywords: Point cloud processing; weld detection; weld quality; fatigue strength Fatigue Design 2025 (FatDes 2025) Processing of point clouds from 3D scans of welded joints for the weld detection and weld quality analysis as input for a reliable fatigue assessment Jo¨rg Baumgartner a, ∗ , Jan Schubnell b , Maria Grace Augustin b a Fraunhofer LBF, Institute for Structural Durability and System Reliability, Bartningstr. 47, 64289 Darmstadt, Germany b Fraunhofer IWM, Institute for Mechanics of Materials, Wo¨hlerstraße 11, 79108 Freiburg, Germany Abstract 3D laser scans are increasingly used for the analysis of the weld quality. Based on these scans, it is possible to determine the weld profiles relevant for the fatigue strength, i.e. the weld angle and notch radii or other features such as spatter or undercuts. These methods are currently being developed and demonstrated in scientific investigations. However, in order to enable the applicability of 3D laser scans for quality assessment in practice, methods are still needed to automatically and reliably identify the weld seam and the weld direction from 3D scans. With this information, the weld profile in 2D cross-sections can be evaluated. Fatigue Design 2025 (FatDes 2025) Processing of point clouds from 3D scans of welded joints for the weld detection and weld quality analysis as input for a reliable fatigue assessment Jo¨rg Baumgartner a, ∗ , Jan Schubnell b , Maria Grace Augustin b a Fraunhofer LBF, Institute for Structural Durability and System Reliability, Bartningstr. 47, 64289 Darmstadt, Germany b Fraunhofer IWM, Institute for Mechanics of Materials, Wo¨hlerstraße 11, 79108 Freiburg, Germany

Nomenclature Nomenclature

r

weld toe radius weld toe angle weld toe radius weld toe angle curvature curvature

t

thickness thickness search distance search distance

r

t

d

α

d

κ α κ

∗ Corresponding author. Tel.: + 49-6151-705-474 E-mail address: joerg.baumgartner@lbf.fraunhofer.de ∗ Corresponding author. Tel.: + 49-6151-705-474 E-mail address: joerg.baumgartner@lbf.fraunhofer.de

2452-3216 © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under the responsibility of Dr Fabien Lefebvre with at least 2 reviewers per paper 10.1016/j.prostr.2025.11.014 2210-7843 © 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http: // creativecommons.org / licenses / by-nc-nd / 4.0 / ) Peer-review under responsibility of the scientific committee of the Fatigue Design 2025 organizers. 2210-7843 © 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http: // creativecommons.org / licenses / by-nc-nd / 4.0 / ) Peer-review under responsibility of the scientific committee of the Fatigue Design 2025 organizers.