Reference : Bead Geometry Prediction in Laser-Wire Additive Manufacturing Process Using Machine L...
Scientific journals : Article
Engineering, computing & technology : Mechanical engineering
Physics and Materials Science
http://hdl.handle.net/10993/50216
Bead Geometry Prediction in Laser-Wire Additive Manufacturing Process Using Machine Learning: Case of Study
English
Mbodj, Natago Guilé mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
Abuabiah, Mohammad mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
Plapper, Peter mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
El Kandaoui, Maxime [Plateforme DRIEG CND and Assembly, Institut de Soudure]
Yaacoubi, Slah [Plateforme DRIEG CND and Assembly, Institut de Soudure]
15-Dec-2021
Applied Sciences
MDPI
Volume 11
Issue 24
Metal Additive Manufacturing and its Applications: From the Material to Components Service Life
Yes (verified by ORBilu)
International
2076-3417
Basel
Switzerland
[en] laser wire additive manufacturing ; machine learning ; bead geometry
[en] In Laser Wire Additive Manufacturing (LWAM), the final geometry is produced using the layer-by-layer deposition (beads principle). To achieve good geometrical accuracy in the final product, proper implementation of the bead geometry is essential. For this reason, the paper focuses on this process and proposes a layer geometry (width and height) prediction model to improve deposition accuracy. More specifically, a machine learning regression algorithm is applied on several experimental data to predict the bead geometry across layers. Furthermore, a neural network-based approach was used to study the influence of different deposition parameters, namely laser power, wire-feed rate and travel speed on bead geometry. To validate the effectiveness of the proposed approach, a test split validation strategy was applied to train and validate the machine learning models. The results show a particular evolutionary trend and confirm that the process parameters have a direct influence on the bead geometry, and so, too, on the final part. Several deposition parameters have been found to obtain an accurate prediction model with low errors and good layer deposition. Finally, this study indicates that the machine learning approach can efficiently be used to predict the bead geometry and could help later in designing a proper controller in the LWAM process.
University of Luxembourg
Interreg V-A Grande Région
Fabrication Additive par Dépôt de Fil” (FAFil) project (Ref. 3477)
Researchers ; Professionals
http://hdl.handle.net/10993/50216
10.3390/app112411949
https://www.mdpi.com/2076-3417/11/24/11949/pdf

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