| 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
Multi-Material Composition Optimization vs Software-Based Single-Material Topology Optimization of a Rectangular Sample under Flexural Load for Fused Deposition Modeling Process
Abstract
Additive manufacturing (AM) technologies have been evolved over the last decade, enabling engineers and researchers to improve functionalities of parts by introducing a growing technology known as multi-material AM. In this context, fused deposition modeling (FDM) process has been modified to create multi-material 3D printed objects with higher functionality. The new technology enables it to combine several types of polymers with hard and soft constituents to make a 3D printed part with improved mechanical properties and functionalities. Knowing this capability, this paper aims to present a parametric optimization method using a genetic algorithm (GA) to find the optimum composition of hard polymer as polylactic acid (PLA) and soft polymer as thermoplastic polyurethane (TPU 95A) used in Ultimaker 3D printer for making a rectangular sample under flexural load in order to minimize the von Mises stress as an objective function. These samples are initially presented in four deferent forms in terms of composition of hard and soft polymers and then, after the optimization process, the final ratio of each type of material will be achieved. Based on the volume fraction of soft polymers in each sample, the equivalent topologically-optimized samples will be obtained that are solely made of single-material PLA as hard polymer under the same flexural load as applied to multi-material samples. Finally, the structural results and manufacturability in terms of the generated support structures, as key element of some AM processes, will be compared for the resultant samples created by two methods of optimization.