| 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
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article
Influence of process parameters on the particle–matrix interaction of WC-Co metal matrix composites produced by laser-directed energy deposition
Abstract
<p>The prediction of the in-service behaviour of metal-matrix composites produced by laser-directed energy deposition is a fundamental challenge in additive manufacturing. The interaction between the reinforcement phase and the matrix has a major impact on the micro and macroscopic properties of these materials. This interaction is fostered by the exposition of the materials to high temperatures. Hence, it is highly influenced by the thermal cycle of the manufacturing process. In this work, an experimental approach is adopted to determine the influence of the main process parameters on the properties of metal-matrix composites. Statistical regression models are employed to consider the role of the most relevant parameters, from exploration to exploitation. The obtained trends are further corroborated by the corresponding microstructural, SEM, and EDS analyses. In terms of surface hardness, the DOE reveals different trends of the response depending on the composition of the feedstock employed. It is concluded that the strengthening behaviour of the material varies throughout the experimental domain studied. When high WC% feedstocks are employed, the main strengthening mechanism responsible for the increase of hardness is the solid-solution of tungsten and carbide precipitation. On the contrary, when low WC%s are employed, grain refinement becomes the main strengthening mechanism.</p>