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doi:10.22028/D291-43175
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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