| 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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Ituarte, Iñigo Flores
Tampere University
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (13/13 displayed)
- 2024Process monitoring by deep neural networks in directed energy deposition : CNN-based detection, segmentation, and statistical analysis of melt poolscitations
- 2024Process monitoring by deep neural networks in directed energy depositioncitations
- 2024Process monitoring by deep neural networks in directed energy deposition:CNN-based detection, segmentation, and statistical analysis of melt poolscitations
- 2023Wire arc additive manufacturing of thin and thick walls made of duplex stainless steelcitations
- 2022Influence of process parameters on the particle–matrix interaction of WC-Co metal matrix composites produced by laser-directed energy depositioncitations
- 2022Towards the additive manufacturing of Ni-Mn-Ga complex devices with magnetic field induced straincitations
- 2021Multi-Material Composition Optimization vs Software-Based Single-Material Topology Optimization of a Rectangular Sample under Flexural Load for Fused Deposition Modeling Processcitations
- 20203D printing of dense and porous TiO 2 structurescitations
- 20203D printing of dense and porous TiO2 structurescitations
- 2019Design and additive manufacture of functionally graded structures based on digital materialscitations
- 2019Effect of process parameters on non-modulated Ni-Mn-Ga alloy manufactured using powder bed fusioncitations
- 2018A decision support system for the validation of metal powder bed-based additive manufacturing applicationscitations
- 2018Digital manufacturing applicability of a laser sintered component for automotive industry: a case studycitations
Places of action
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article
Design and additive manufacture of functionally graded structures based on digital materials
Abstract
Voxel-based multimaterial jetting additive manufacturing allows fabrication of digital materials (DMs) at the meso-scale (∼1 mm) by controlling the deposition patterns of soft elastomeric and rigid glassy polymers at the voxel-scale (∼90 μm). The digital materials can then be used to create heterogeneous functionally graded material (FGM) structures at the macro-scale (∼10 mm) programmed to behave in a predefined manner. This offers huge potential for design and fabrication of novel and complex bespoke mechanical structures. This paper presents a complete design and manufacturing workflow that simultaneously integrates material design, structural design, and product fabrication of FGM structures based on digital materials. This is enabled by a regression analysis of the experimental data on mechanical performance of the DMs i.e., Young’s modulus, tensile strength and elongation at break. This allows us to express the material behavior simply as a function of the microstructural descriptors (in this case, just volume fraction) without having to understand the underlying microstructural mechanics while simultaneously connecting it to the process parameters. Our proposed design and manufacturing approach is then demonstrated and validated in two series of design exercises to devise complex FGM structures. First, we design, computationally predict and experimentally validate the behavior of prescribed designs of FGM tensile structures with different material gradients. Second, we present a design automation approach for optimal FGM structures. The comparison between the simulations and the experiments with the FGM structures shows that the presented design and fabrication workflow based on our modeling approach for DMs at meso-scale can be effectively used to design and predict the performance of FGMs at macro-scale.