| 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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Helsen, Jan
Vrije Universiteit Brussel
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (9/9 displayed)
- 2023Experimental evaluation of the metal powder particle flow on the melt pool during directed energy depositioncitations
- 2021Prediction of build geometry for DED using supervised learning methods on simulated process monitoring datacitations
- 2021An interdisciplinary framework to predict premature roller element bearing failures in wind turbine gearboxescitations
- 2021An interdisciplinary framework to predict premature roller element bearing failures in wind turbine gearboxescitations
- 2020Spatial distributed spectroscopic monitoring of melt pool and vapor plume during the laser metal deposition processcitations
- 2020MiCLAD as a platform for real-time monitoring and machine learning in laser metal depositioncitations
- 2018Robust Power-Electronic-Converter-Fault Detection and Isolation Technique for DFIG Wind Turbines
- 2016Experimental Investigation of Bearing Slip in a Wind Turbine Gearbox During a Transient Grid Loss Eventcitations
- 2016Experimental dynamic identification of modeshape driving wind turbine grid loss event on nacelle testrigcitations
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.