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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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Ulbricht, Alexander
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (19/19 displayed)
- 2024Determination of short carbon fiber orientation in zirconium diboride ceramic matrix compositescitations
- 2023Evolution of Creep Damage of 316L Produced by Laser Powder Bed Fusioncitations
- 2023Multi-scale correlation between defects and internal stresses in additively manufactured AISI316L structures ; Mehrskalige Korrelation zwischen Defekten und Spannungen in additiv gefertigten Strukturen aus Edelstahl 316L (1.4404)
- 2023Micro-CT analysis and mechanical properties of low dimensional CFR-PEEK specimens additively manufactured by material extrusioncitations
- 2022Creep and creep damage behavior of stainless steel 316L manufactured by laser powder bed fusioncitations
- 2022On the registration of thermographic in situ monitoring data and computed tomography reference data in the scope of defect prediction in laser powder bed fusioncitations
- 2022Capability to detect and localize typical defects of laser powder bed fusion (L-PBF) process: an experimental investigation with different non-destructive techniquescitations
- 2021Can Potential Defects in LPBF Be Healed from the Laser Exposure of Subsequent Layers? A Quantitative Studycitations
- 2021Scanning manufacturing parameters determining the residual stress state in LPBF IN718 small partscitations
- 2021Mechanical anisotropy of additively manufactured stainless steel 316L: An experimental and numerical studycitations
- 2021Investigation of the thermal history of L-PBF metal parts by feature extraction from in-situ SWIR thermographycitations
- 2021Towards the optimization of post-laser powder bed fusion stress-relieve treatments of stainless steel 316Lcitations
- 2021Can potential defects in LPBF be healed from the laser exposure of subsequent layers?citations
- 2021Diffraction-based residual stress characterization in laser additive manufacturing of metalscitations
- 2020Separation of the Formation Mechanisms of Residual Stresses in LPBF 316L
- 2020Separation of the Formation Mechanisms of Residual Stresses in LPBF 316Lcitations
- 2020On the interplay of microstructure and residual stress in LPBF IN718citations
- 2020In-Situ Defect Detection in Laser Powder Bed Fusion by Using Thermography and Optical Tomography—Comparison to Computed Tomographycitations
- 2019The Influence of the Temperature Gradient on the Distribution of Residual Stresses in AM AISI 316L
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article
In-Situ Defect Detection in Laser Powder Bed Fusion by Using Thermography and Optical Tomography—Comparison to Computed Tomography
Abstract
Among additive manufacturing (AM) technologies, the laser powder bed fusion (L-PBF) is one of the most important technologies to produce metallic components. The layer-wise build-up of components and the complex process conditions increase the probability of the occurrence of defects. However, due to the iterative nature of its manufacturing process and in contrast to conventional manufacturing technologies such as casting, L-PBF offers unique opportunities for in-situ monitoring. In this study, two cameras were successfully tested simultaneously as a machine manufacturer independent process monitoring setup: a high-frequency infrared camera and a camera for long time exposure, working in the visible and infrared spectrum and equipped with a near infrared filter. An AISI 316L stainless steel specimen with integrated artificial defects has been monitored during the build. The acquired camera data was compared to data obtained by computed tomography. A promising and easy to use examination method for data analysis was developed and correlations between measured signals and defects were identified. Moreover, sources of possible data misinterpretation were specified. Lastly, attempts for automatic data analysis by data integration are presented.