Please use this identifier to cite or link to this item: doi:10.22028/D291-43175
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Title: Comparison of machine learning based methods on prediction quality of thin-walled geometries using laser-based Direct Energy Deposition
Author(s): Paulus, Pascal
Ruppert, Yannick
Andreicovici, Alfred
Vielhaber, Michael
Griebsch, Jürgen
Editor(s): Schmidt, M.
Arnold, C.B.
Wudy, K.
Language: English
Title: Procedia CIRP
Volume: 124
Pages: 781-784
Publisher/Platform: Elsevier
Year of Publication: 2024
Place of publication: Amsterdam
Place of the conference: Fürth, Germany
Free key words: Machine learning
Direct Energy Deposition
Prediction quality
Process development
DDC notations: 620 Engineering and machine engineering
Publikation type: Conference Paper
Abstract: Laser-based Direct Energy Deposition of small parts with high build-up rates deals with complex relationships of the process parameters and constantly varying boundary conditions. To avoid dimensional deviations during the build-up, the process parameters must be adjusted. These geometric deviations are based on the thermal conditions determined by the energy input of the previous layers. Machine learning algorithms can be used to identify appropriate process parameters, enabling economic and resource-efficient process development. The aim of this work is to compare LSTM (Long Short-Term Memory) and XGBoost (Extreme Gradient Boosting) as single model and model chain, regarding their prediction accuracy of the laser power for the layered wall build-up with a constant wall width. The model performance is calculated based on newly manufactured test samples. The results show that the chained LSTM has the highest deviation, whereas the XGBoost algorithm proves to be the most accurate.
DOI of the first publication: 10.1016/j.procir.2024.08.224
URL of the first publication: https://www.sciencedirect.com/science/article/pii/S221282712400578X
Link to this record: urn:nbn:de:bsz:291--ds-431754
hdl:20.500.11880/38727
http://dx.doi.org/10.22028/D291-43175
ISSN: 2212-8271
Date of registration: 14-Oct-2024
Notes: Procedia CIRP, Volume 124, 2024, Pages 781-784
Faculty: NT - Naturwissenschaftlich- Technische Fakultät
Department: NT - Systems Engineering
Professorship: NT - Prof. Dr. Michael Vielhaber
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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