693.932 PEOPLE
| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
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
| Organizations | Location | People |
|---|