| 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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Jardon, Zoé
Vrije Universiteit Brussel
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (12/12 displayed)
- 2023Experimental evaluation of the metal powder particle flow on the melt pool during directed energy depositioncitations
- 2023Comparison and Analysis of Hyperspectral Temperature Data in Directed Energy Depositioncitations
- 2022Numerical and experimental study of a crack localisation system embedded in 3D printed smart metallic components
- 2022Powder-Gas Jet Velocity Characterization during Coaxial Directed Energy Deposition Processcitations
- 2021Prediction of build geometry for DED using supervised learning methods on simulated process monitoring datacitations
- 2021Structural health monitoring through surface acoustic wave inspection deployed on capillaries embedded in additively manufactured components
- 2021Process parameter study for enhancement of directed energy deposition powder efficiency based on single-track geometry evaluationcitations
- 2021Production Assessment of Hybrid Directed Energy Deposition Manufactured Sample with Integrated Effective Structural Health Monitoring channel (eSHM)citations
- 2020Offline powder-gas nozzle jet characterization for coaxial laser-based Directed Energy Depositioncitations
- 2019On the Influence of Capillary-Based Structural Health Monitoring on Fatigue Crack Initiation and Propagation in Straight Lugscitations
- 2018Effective Structural Health Monitoring through the Monitoring of Pressurized Capillaries in Additive Manufactured Materials
- 2017Proof of Concept of Integrated Load Measurement in 3D Printed Structurescitations
Places of action
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article
Prediction of build geometry for DED using supervised learning methods on simulated process monitoring data
Abstract
One of today’s ongoing challenges in directed energy deposition (DED) is controlling the geometry and material properties of parts. This manufacturing process is complex and nonlinear due to multiple physical phenomena at play and is therefore hard to model analytically. Machine learning (ML) on the contrary is particularly well suited to predict the behavior of a complex process with multiple inputs and outputs such as DED. A significant amount of data is required to train machine learning models, but experimental data are costly time-wise and should therefore be produced in an intelligent way. As a stepping stone for the future production of experimental training data, a finite element model of the process was developed in this study as an unlimited source of training data for the ML models. This model takes into account the printing parameters (laser speed, laser power, and powder flow rate) and outputs’ simulated process monitoring data thanks to a postprocessing method that is outlined in this article. A dataset was produced by simulating 102 tracks in 316L stainless steel with the model. From the analysis of this dataset, it was shown that K-nearest neighbors, support vector regression, decision tree regression, linear regression, and artificial neural network models are all capable of modelling the relationship between the printing parameters and the melt pool characteristics effectively.