| 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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article
Integrated modeling of heat transfer, shear rate, and viscosity for simulation-based characterization of polymer coalescence during material extrusion
Abstract
The material extrusion process (MEX), also known as the fused filament fabrication process, has attracted attention in the manufacturing industry. A major obstacle to further application of the technology is the lack of mechanical strength due to the weak interlayer strength and poor coalescence between the adjacent beads. Understanding the effect of printing parameters on the coalescence of the adjacent beads is a step toward the improvement of the process. In this study, a novel two-phase flow numerical simulation approach coupled with heat transfer simulation has been applied to the high-viscosity polymers to determine the coalescence in the MEX process. The influence of printing temperature, substrate temperature, and the temperature of the printing chamber, as well as material deposition strategy (unidirectional and bidirectional) on the coalescence of the beads, has been investigated by numerical simulation and validated by experimental study. The modeling approach is applied to Glycerol, Polyether ether ketone (PEEK) and Polylactic acid (PLA). The results show that increasing temperature points (substrate temperature, chamber temperature, and printing temperature), increase the coalescence between the beads in the MEX process. The heat transfer model reveals that the cooling rate of the deposited bead in the MEX process is relatively high, and hence, the time window for reaching the coalescence between beads/layers is short. The heat transfer model also indicates that deposition of the further layers and beads does not influence the coalescence. The coalescence in the bidirectional deposition is higher compared to the unidirectional all conditions remaining similar. Unidirectional deposition leads to a uniform coalescence between the beads. However, the coalescence is not uniform for bidirectional deposition. The main novelty of this research is to simultaneously model heat transfer, shear rate and coalescence for numerical simulation to study the effect of printing parameters on the coalescence in the MEX process. Since the modeling of coalescence is time-consuming, two empirical equations based on obtained results have been proposed to predict the coalescence for PLA and PEEK separately.