| 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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Mokhtarian, Hossein
Tampere University
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (12/12 displayed)
- 2024Assessing the Effect of Infill Strategies on Hardness Properties of Cuboidal Parts Printed with Wire and Arc Additive Manufacturing
- 2024Process monitoring by deep neural networks in directed energy deposition : CNN-based detection, segmentation, and statistical analysis of melt poolscitations
- 2024Integrating dimensional and scaling analyses with functional modelling and graphs: An approach to comprehend mass transfer in welding
- 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
- 2023Assessing the Effect of Infill Strategies on Hardness Properties of Cuboidal Parts Printed with Wire and Arc Additive Manufacturing
- 2023Integrated modeling of heat transfer, shear rate, and viscosity for simulation-based characterization of polymer coalescence during material extrusioncitations
- 2023Integrated modeling of heat transfer, shear rate, and viscosity for simulation-based characterization of polymer coalescence during material extrusioncitations
- 2018Knowledge-based optimization of artificial neural network topology for additive manufacturing process modeling: a case study for fused deposition modelingcitations
- 2018Knowledge-based optimization of artificial neural network topology for process modeling of fused deposition modelingcitations
- 2018Knowledge based optimization of artificial neural network topology for additive manufacturing process modeling: a case study for fused deposition modelingcitations
- 2018Industrialization of hybrid and additive manufacturing - Implementation to Finnish industry (HYBRAM)
Places of action
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report
Industrialization of hybrid and additive manufacturing - Implementation to Finnish industry (HYBRAM)
Abstract
This report summarizes the main findings of the public part of the HYBRAM Research Benefit project carried out in 2016-2018, where the aim was to identify the main barriers for implementing Additive Manufacturing technologies in the Finnish manufacturing industry. The features and limitations of laser powder bed fusion, directed energy deposition and hybrid manufacturing technologies are analyzed to provide an overview of the capabilities of modern machines as well as to describe the limitations. The state-of-the-art of automation and integration capabilities of AM production systems are reported. Industrial demonstrator parts were studied to assess the applicability of AM as a production method from the design, technical feasibility, cost and quality point of views. The report also contains a chapter dedicated to explaining the dimensional analysis conceptual modeling (DACM) framework as a modeling approach for AM.