Blug, Andreas ; Conrad, Fabian ; Bertz, Alexander ; Kontermann, Christian ; Carl, Daniel ; Oechsner, Matthias (2024)
Application of high‐performance DIC for a comprehensive evaluation of biaxial fatigue crack growth experiments.
In: Strain : An International Journal for Experimental Mechanics, 2023, 59 (6)
doi: 10.26083/tuprints-00027173
Artikel, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (Abstract)
Precise determination of the remaining service life of technical components requires sufficient knowledge of fatigue crack growth behaviour and the growth rate of defects. Cracks in real components often experience multiaxial far field stresses due to their complex geometry and composite loadings acting on it. Digital image correlation (DIC) is well established for crack length and displacement measurements, but it usually requires sample preparation with speckle paint and interferes with mechanical extensometers. To overcome these limitations, we use a novel 2D DIC system combining a graphics processing unit (GPU) with a CoaXPress 2.0 camera, acquiring up to 3 GB/s of image data. It enables real‐time evaluation of both integral strain like an extensometer and full‐field DIC on images selected automatically in real‐time. This combination enables the use of one single sensor for strain‐controlled testing and fatigue crack growth characterisation. The full‐field displacement is compared to a finite‐element model (FEM) simulating the actual crack contour measured by the DIC system. The results show that high‐performance DIC has the potential to simultaneously simplify crack‐growth experiments and provide comprehensive fracture mechanical information.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Blug, Andreas ; Conrad, Fabian ; Bertz, Alexander ; Kontermann, Christian ; Carl, Daniel ; Oechsner, Matthias |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Application of high‐performance DIC for a comprehensive evaluation of biaxial fatigue crack growth experiments |
| Sprache: | Englisch |
| Publikationsjahr: | 4 Juni 2024 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | Dezember 2023 |
| Ort der Erstveröffentlichung: | Oxford |
| Verlag: | Wiley-Blackwell |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Strain : An International Journal for Experimental Mechanics |
| Jahrgang/Volume einer Zeitschrift: | 59 |
| (Heft-)Nummer: | 6 |
| Kollation: | 15 Seiten |
| DOI: | 10.26083/tuprints-00027173 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/27173 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (Abstract): | Precise determination of the remaining service life of technical components requires sufficient knowledge of fatigue crack growth behaviour and the growth rate of defects. Cracks in real components often experience multiaxial far field stresses due to their complex geometry and composite loadings acting on it. Digital image correlation (DIC) is well established for crack length and displacement measurements, but it usually requires sample preparation with speckle paint and interferes with mechanical extensometers. To overcome these limitations, we use a novel 2D DIC system combining a graphics processing unit (GPU) with a CoaXPress 2.0 camera, acquiring up to 3 GB/s of image data. It enables real‐time evaluation of both integral strain like an extensometer and full‐field DIC on images selected automatically in real‐time. This combination enables the use of one single sensor for strain‐controlled testing and fatigue crack growth characterisation. The full‐field displacement is compared to a finite‐element model (FEM) simulating the actual crack contour measured by the DIC system. The results show that high‐performance DIC has the potential to simultaneously simplify crack‐growth experiments and provide comprehensive fracture mechanical information. |
| Freie Schlagworte: | digital image correlation, fatigue crack growth, finite‐element model, strain‐control, uni‐ and biaxial loading |
| ID-Nummer: | Artikel-ID: e12455 |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-271739 |
| Zusätzliche Informationen: | This article also appears in: New Trends in Machine Learning, Data-Driven Approaches, and High-performance Computing for Experimental Mechanics |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 500 Naturwissenschaften und Mathematik > 530 Physik 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Fachgebiet und Institut für Werkstoffkunde - Zentrum für Konstruktionswerkstoffe - Staatliche Materialprüfungsanstalt Darmstadt (IfW-MPA) |
| Hinterlegungsdatum: | 04 Jun 2024 12:41 |
| Letzte Änderung: | 05 Jun 2024 09:35 |
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| Suche nach Titel in: | TUfind oder in Google |
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