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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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Zanger, Prof. Dr.-Ing. Frederik
Karlsruhe Institute of Technology
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (9/9 displayed)
- 2024Influence of TiC-Nanoparticles on the material properties of AlSi10Mg manufactured by Laser Powder Bed Fusion
- 2023Application of Bayesian Optimization for the optimized development of slurries for Additive Manufacturing
- 2023Customized production of highly integrated 3D power electronic modules by multi-material vat photopolymerization, powder bed fusion and selective piezojet metallization [MultiPower]
- 2022Exploring the Applicability of Sinterjoining to Combine Additively Manufactured Ceramic Componentscitations
- 2022Process Combination of VPP-LED and Vacuum Die Casting for Producing Complex Ceramic 3D-MID
- 2021Dual-Laser PBF-LB Processing of a High-Performance Maraging Tool Steel FeNiCoMoVTiAlcitations
- 2020Effect of tool coatings on surface grain refinement in orthogonal cutting of AISI 4140 steelcitations
- 2020Complementary Machining: Effect of tool types on tool wear and surface integrity of AISI 4140citations
- 2019Influence of anisotropy of additively manufactured AlSi10Mg parts on chip formation during orthogonal cuttingcitations
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document
Application of Bayesian Optimization for the optimized development of slurries for Additive Manufacturing
Abstract
Additive manufacturing (AM) by vat photopolymerisation (VPP) enables the flexible production of ceramic and metallic components through a layer-by-layer build-up followed by debinding and sintering. In this process, slurries consisting of a photosensitive binder system and ceramic or metallic powder are cured locally by selective application of light. In order for the slurry to be processed in VPP systems, its viscosity must be sufficiently low. At the same time, the slurries must harden well for shaping. Both properties are generally worsened by a higher ceramic or metallic filler content. However, in order to accelerate the debinding and sintering process and to increase the process reliability, the aim is to maximize the filler content. This conflict of objectives requires a precise adjustment of the large amount of slurry constituents. Hence, an experimental slurry development and optimization is very expensive and time-consuming. Therefore, the application of artificial intelligence (AI) seemed to be a promising approach. In this work, Bayesian optimization was used to iteratively optimize the slurry composition. Using this approach for ceramic slurries, it was possible to achieve in less than 40 optimization steps an aluminum oxide (Al2O3) slurry suitable for vat photopolymerisation with a volume fraction of 65 % ceramic powder, the highest currently known fraction for Al2O3 in VPP slurries