| 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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document
MiCLAD as a platform for real-time monitoring and machine learning in laser metal deposition
Abstract
The MiCLAD machine designed at the VUB, Belgium, allows for closed-loop controlled laser metal deposition including various in-situ optical based measurement systems. These integrated sensors collect information on deposition geometry and temperature during the building process. Hence, each cubic millimeter of material that is either added or removed is mapped to its digital twin with a millisecond temporal resolution in the machines database. This paper introduces the platform and its capabilities by focusing on the procedure of obtaining the necessary training data for the future application of machine learning algorithms, with the goal of controlling the geometry and temperature history during additive manufacturing.