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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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Lechner, Philipp
University of Augsburg
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (5/5 displayed)
- 2023Investigation of Multi-Material Liquid Metal Jetting with copper materials
- 2021A Method for Characterising the Influence of Casting Parameters on the Metallurgical Bonding of Copper and Steel Bimetalscitations
- 2021Analysis of salts for use as support structure in metal material jettingcitations
- 2021Feasibility of Acoustic Print Head Monitoring for Binder Jetting Processes with Artificial Neural Networkscitations
- 2021Influence of Salt Support Structures on Material Jetted Aluminum Partscitations
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article
Feasibility of Acoustic Print Head Monitoring for Binder Jetting Processes with Artificial Neural Networks
Abstract
Art. 10672, 16 S. ; The clogging of piezoelectric nozzles is a typical problem in various additive binder jetting processes, such as the manufacturing of casting molds. This work aims at print head monitoring in these binder jetting processes. The structure-born noise of piezoelectric print modules is analyzed with an Artificial Neural Network to classify whether the nozzles are functional or clogged. The acoustic data are studied in the frequency domain and utilized as input for an Artificial Neural Network. We found that it is possible to successfully classify individual nozzles well enough to implement a print head monitoring, which automatically determines whether the print head needs maintenance. ; 11 ; Nr.22