| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Wegener, Thomas
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (24/24 displayed)
- 2024On the fatigue behavior of a tool steel manufactured by powder bed based additive manufacturing—a comparison between electron- and laserbeam processed AISI H13citations
- 2024Pathways toward the Use of Non-Destructive Micromagnetic Analysis for Porosity Assessment and Process Parameter Optimization in Additive Manufacturing of 42CrMo4 (AISI 4140)citations
- 2024Influence of Defects and Microstructure on the Thermal Expansion Behavior and the Mechanical Properties of Additively Manufactured Fe-36Nicitations
- 2024Micro-macro modeling of tensile behavior of a friction stir welded hybrid joint of AlSi10Mg parts produced by powder bed fusion and castingcitations
- 2024Influence of deep rolling at different temperatures on near-surface and mechanical properties of a Maraging C250 steelcitations
- 2024On the cyclic deformation behavior of wire-based directed energy deposited Fe-Ni Invar alloycitations
- 2024A Pragmatic Approach for Rapid, Non-Destructive Assessment of Defect Types in Laser Powder Bed Fusion Based on Melt Pool Monitoring Data
- 2023On the Reliability of Automated Analysis of Fracture Surfaces Using a Novel Computer Vision‐Based Toolcitations
- 2023On the structural integrity and fatigue performance of additively manufactured Ti-6Al-4V parts processed using mechanically recycled powderscitations
- 2023On the low-cycle fatigue behavior of a multi-phase high entropy alloy with enhanced plasticitycitations
- 2023Experimental Analysis of the Stability of Retained Austenite in a Low‐Alloy 42CrSi Steel after Different Quenching and Partitioning Heat Treatmentscitations
- 2023A comparative study using water atomized and gas atomized powder in laser powder bed fusion – Assessment of the fatigue performancecitations
- 2023Micromechanical modeling of the low-cycle fatigue behavior of additively manufactured AlSi10Mgcitations
- 2022Micromechanical Modeling of AlSi10Mg Processed by Laser-Based Additive Manufacturing: From as-Built to Heat-Treated Microstructurescitations
- 2022Micromechanical Modeling of AlSi10Mg Processed by Laser-Based Additive Manufacturing: From as-Built to Heat-Treated Microstructures
- 2022Fatigue crack growth in additively manufactured Hastelloy X - Influences of crack orientation and post-fabrication treatmentscitations
- 2022Micromechanical Modeling of AlSi10Mg Processed by Laser-Based Additive Manufacturing:From as-Built to Heat-Treated Microstructurescitations
- 2022On the Friction Stir Processing of Additive‐Manufactured 316L Stainless Steelcitations
- 2021Consequences of Deep Rolling at Elevated Temperature on Near-Surface and Fatigue Properties of High-Manganese TWIP Steel X40MnCrAl19-2citations
- 2021On the fatigue behavior of differently deep rolled conditions of SAE 1045 in the very-high-cycle fatigue regimecitations
- 2021On the influence of build orientation on properties of friction stir welded AleSi10Mg parts produced by selective laser melting
- 2021Thermal Stability of Residual Stresses in Differently Deep Rolled Surface Layers of Steel SAE 1045citations
- 2020On the structural integrity of Fe-36Ni Invar alloy processed by selective laser melting
- 2019Effect of grain size on the very high cycle fatigue behavior and notch sensitivity of titaniumcitations
Places of action
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article
On the Reliability of Automated Analysis of Fracture Surfaces Using a Novel Computer Vision‐Based Tool
Abstract
<jats:p>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.</jats:p>