| 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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Nguyen, Vu
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (16/16 displayed)
- 2024Advances in Additive Manufacturing of Auxetic Structures for Biomedical Applicationscitations
- 2024Analysis of self-supporting conformal cooling channels additively manufactured by hybrid directed energy deposition for IM toolingcitations
- 2023Advances in Multiscale Modelling of Metal Additive Manufacturing
- 2023Osseointegrability of 3D-printed porous titanium alloy implant on tibial shaft bone defect in rabbit modelcitations
- 2022Directed-energy deposition (DED) of Ti-6Al-4V alloy using fresh and recycled feedstock powders under reactive atmosphere
- 2021Progress Towards a Complete Model of Metal Additive Manufacturingcitations
- 2019Measurement of Laser Absorptivity by Calibrated Melt Pool Simulation
- 2019Residual Stress in Additive Manufacture
- 2018Accelerating Experimental Design by Incorporating Experimenter Hunchescitations
- 2017Modelling Powder Flow in Metal Additive Manufacturing Systems
- 2017A desktop computer model of the arc, weld pool and workpiece in metal inert gas weldingcitations
- 2017Aiming for modeling-assisted tailored designs for additive manufacturingcitations
- 2015A desktop computer model of arc welding using a CFD approach
- 2015Prediction of springback in anisotropic sheet metals: The effect of orientation and frictioncitations
- 2011Modelling die filling in ultra-thin aluminium die castings
- 20113D thermo-mechanical modelling of wheel and belt continuous castingcitations
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document
Modelling Powder Flow in Metal Additive Manufacturing Systems
Abstract
In powder-bed based metal additive manufacturing applications, the addition of the powder layers is the crucial first step in building up of the part in 3D and has a significant impact on final part quality. A common technique employed is to add successive layers of metal powder by raking a new layer across the existing surface. Understanding this raking process and how the properties of the powder particles (e.g. size, shape, density, interaction properties) and process parameters (e.g. height of powder layer, rake geometry, rake speed) affect the properties of the bed after raking is crucial in optimizing the performance of the system and ensuring the quality of the 3D-printed part. We will present results of a computational model of this raking process using the discrete element method (DEM). This model directly incorporates the powder’s particle size distribution, particle shapes and experimental measurements of the powder flowability. We have applied this model to simulating raking of both Arcam Ti powder and CSIRO Manipulated Ti Powder and it is being applied in improving the performance of existing 3D powder-bed systems and exploring new rake designs and powder morphology combinations in order to deliver products with improved performance. We will also describe how this model fits within a complete modelling framework we are developing for the other key physical processes in powder based metal additive manufacturing including the transfer of energy from the laser or electron beam to the metal, the melting and solidification of the powder, the flow of liquid metal in the melt pool, the heat transfer from the melt pool to the surrounding powder and solid metal, the evolution of the microstructure of the component, and the residual stress and deformation of the component that result from the non-uniform heating and cooling.