| 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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Ertveldt, Julien
Vrije Universiteit Brussel
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (16/16 displayed)
- 2023Measuring and Predicting the Effects of Residual Stresses from Full-Field Data in Laser-Directed Energy Depositioncitations
- 2023Comparison and Analysis of Hyperspectral Temperature Data in Directed Energy Depositioncitations
- 2022Experimental identification of process dynamics for real-time control of directed energy depositioncitations
- 2022FPGA-based visual melt-pool monitoring with pyrometer correlation for geometry and temperature measurement during Laser Metal Depositioncitations
- 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
- 2020MiCLAD as a platform for real-time monitoring and machine learning in laser metal depositioncitations
- 2020Comparison of visual and hyperspectral monitoring of the melt pool during Laser Metal Deposition
- 2020Offline powder-gas nozzle jet characterization for coaxial laser-based Directed Energy Depositioncitations
- 2019Analytical Modeling of Embedded Load Sensing Using Liquid-Filled Capillaries Integrated by Metal Additive Manufacturingcitations
- 2019On the Influence of Capillary-Based Structural Health Monitoring on Fatigue Crack Initiation and Propagation in Straight Lugscitations
- 2016Vibration Monitoring Using Fiber Optic Sensors in a Lead-Bismuth Eutectic Cooled Nuclear Fuel Assemblycitations
- 2016Reconstruction of impacts on a composite plate using fiber Bragg gratings (FBG) and inverse methodscitations
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.