Datum
2023-08-05Autor
Engelhardt, AnnaDecke, JensMeier, DavidDulig, FranzRagunathan, RishanWegener, ThomasSick, BernhardNiendorf, ThomasSchlagwort
620 Ingenieurwissenschaften 660 Chemische Verfahrenstechnik, Technische Chemie Rapid Prototyping <Fertigung>BruchflächeBilderkennungObjekterkennungRasterelektronenmikroskopTiAl6V4Metadata
Zur Langanzeige
Aufsatz![](/themes/Mirage2/images/Open_Access_logo_PLoS_transparent.svg)
Artikel (Publikationen im Open Access gefördert durch die UB)
On the Reliability of Automated Analysis of Fracture Surfaces Using a Novel Computer Vision-Based Tool
Zusammenfassung
Fracture surface analysis is of utmost importance with respect to structural integrity of metallic materials. This especially holds true for additively manufactured materials. Despite an increasing trend of automatization of testing methods, the analysis and classification of fatigue fracture surface images is commonly done manually by experts. Although this leads to correct results in most cases, it has several disadvantages, e.g., the need of a huge knowledge base to interpret images correctly. In present work, an unsupervised tool for analysis of overview images of fatigue fracture surface images is developed to support nonexperienced users to identify the origin of the fracture. The tool is developed using fracture surface images of additively manufactured Ti6Al4V specimens fatigued in the high-cycle-fatigue regime and is based on the identification of river marks. Several recording parameters seem to have no significant influence on the results as long as preprocessing settings are adapted. Moreover, it is possible to analyze images of other materials with the tool as long as the fracture surfaces contain river marks. However, special features like multiple origins or origins located in direct vicinity to the surface, e.g., caused by increased plastic strains, require a further tool development or alternative approaches.
Zitierform
In: Advanced Engineering Materials Volume 25 / Issue 21 (2023-08-05) eissn:1527-2648Förderhinweis
Gefördert im Rahmen des Projekts DEALSammlung(en)
Publikationen (Fachgebiet Metallische Werkstoffe)Artikel (Publikationen im Open Access gefördert durch die UB)
Zitieren
@article{doi:10.17170/kobra-202311159019,
author={Engelhardt, Anna and Decke, Jens and Meier, David and Dulig, Franz and Ragunathan, Rishan and Wegener, Thomas and Sick, Bernhard and Niendorf, Thomas},
title={On the Reliability of Automated Analysis of Fracture Surfaces Using a Novel Computer Vision-Based Tool},
journal={Advanced Engineering Materials},
year={2023}
}
0500 Oax 0501 Text $btxt$2rdacontent 0502 Computermedien $bc$2rdacarrier 1100 2023$n2023 1500 1/eng 2050 ##0##http://hdl.handle.net/123456789/15256 3000 Engelhardt, Anna 3010 Decke, Jens 3010 Meier, David 3010 Dulig, Franz 3010 Ragunathan, Rishan 3010 Wegener, Thomas 3010 Sick, Bernhard 3010 Niendorf, Thomas 4000 On the Reliability of Automated Analysis of Fracture Surfaces Using a Novel Computer Vision-Based Tool / Engelhardt, Anna 4030 4060 Online-Ressource 4085 ##0##=u http://nbn-resolving.de/http://hdl.handle.net/123456789/15256=x R 4204 \$dAufsatz 4170 5550 {{Rapid Prototyping <Fertigung>}} 5550 {{Bruchfläche}} 5550 {{Bilderkennung}} 5550 {{Objekterkennung}} 5550 {{Rasterelektronenmikroskop}} 5550 {{TiAl6V4}} 7136 ##0##http://hdl.handle.net/123456789/15256
2023-12-01T15:33:19Z 2023-12-01T15:33:19Z 2023-08-05 doi:10.17170/kobra-202311159019 http://hdl.handle.net/123456789/15256 Gefördert im Rahmen des Projekts DEAL eng Namensnennung 4.0 International http://creativecommons.org/licenses/by/4.0/ additive manufacturing image recognition object detection scanning electron microscopes Ti6Al4V 620 660 On the Reliability of Automated Analysis of Fracture Surfaces Using a Novel Computer Vision-Based Tool Aufsatz Fracture surface analysis is of utmost importance with respect to structural integrity of metallic materials. This especially holds true for additively manufactured materials. Despite an increasing trend of automatization of testing methods, the analysis and classification of fatigue fracture surface images is commonly done manually by experts. Although this leads to correct results in most cases, it has several disadvantages, e.g., the need of a huge knowledge base to interpret images correctly. In present work, an unsupervised tool for analysis of overview images of fatigue fracture surface images is developed to support nonexperienced users to identify the origin of the fracture. The tool is developed using fracture surface images of additively manufactured Ti6Al4V specimens fatigued in the high-cycle-fatigue regime and is based on the identification of river marks. Several recording parameters seem to have no significant influence on the results as long as preprocessing settings are adapted. Moreover, it is possible to analyze images of other materials with the tool as long as the fracture surfaces contain river marks. However, special features like multiple origins or origins located in direct vicinity to the surface, e.g., caused by increased plastic strains, require a further tool development or alternative approaches. open access Engelhardt, Anna Decke, Jens Meier, David Dulig, Franz Ragunathan, Rishan Wegener, Thomas Sick, Bernhard Niendorf, Thomas doi:10.1002/adem.202300876 Rapid Prototyping <Fertigung> Bruchfläche Bilderkennung Objekterkennung Rasterelektronenmikroskop TiAl6V4 publishedVersion eissn:1527-2648 Issue 21 Advanced Engineering Materials Volume 25 false 2300876
Die folgenden Lizenzbestimmungen sind mit dieser Ressource verbunden: