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See detailModeling and Control of Laser Wire Additive Manufacturing
Mbodj, Natago Guilé UL

Doctoral thesis (2022)

Metal Additive Manufacturing (MAM) offers many advantages such as fast product manufacturing, nearly zero material waste, prototyping of complex large parts and the automatization of the manufacturing ... [more ▼]

Metal Additive Manufacturing (MAM) offers many advantages such as fast product manufacturing, nearly zero material waste, prototyping of complex large parts and the automatization of the manufacturing process in the aerospace, automotive and other sectors. In the MAM, several parameters influence the product creation steps, making the MAM challenging. In this thesis, we modelize and control the deposition process for a type of MAM where a laser beam is used to melt a metallic wire to create the metal parts called the Laser Wire Additive Manufacturing Process (LWAM). In the dissertation, first, a novel parametric modeling approach is created. The goal of this approach is to use parametric product design features to simulate and print 3D metallic objects for the LWAM. The proposed method includes a pattern and the robot toolpath creation while considering several process requirements of LWAM, such as the deposition sequences and the robot system. This technique aims to develop adaptive robot toolpaths for a precise deposition process with nearly zero error in the product creation process. Second, a layer geometry (width and height) prediction model to improve deposition accuracy is proposed. A machine learning regression algorithm is applied to 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. The experimental results shows that the model has an error rate of (i.e., 2∼4%). Third, a physics-based model of the bead geometry including known process parameters and material properties was created. The model developed for the first time includes critical process parameters, the material properties and the thermal history to describe the relationship between the layer height with different process inputs (i.e., the power, the standoff distance, the temperature, the wire-feed rate and the travel speed). The numerical results show a match of the model with the experimental measurements. Finally, a Model Predictive Controller (MPC) was designed to keep the layer height trajectory constant, considering the constraints and the operating range of the parameters of the process inputs. The model simulation result shows an acceptable tracking of the reference height. [less ▲]

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See detailBead Geometry Prediction in Laser-Wire Additive Manufacturing Process Using Machine Learning: Case of Study
Mbodj, Natago Guilé UL; Abuabiah, Mohammad UL; Plapper, Peter UL et al

in Applied Sciences (2021), Volume 11(Issue 24),

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 ... [more ▼]

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. [less ▲]

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See detailBead Width Prediction in Laser Wire Additive Manufacturing Process
Mbodj, Natago Guilé UL; Plapper, Peter UL

in Mbodj, Natago Guilé; Plapper, Peter (Eds.) Bead Width Prediction in Laser Wire Additive Manufacturing Process (2021, October 22)

In laser wire Additive manufacturing (LWAM), the final geometry is produced using layer-by-layer deposition principle of beads. To achieve good geometrical accuracy of the final product, proper ... [more ▼]

In laser wire Additive manufacturing (LWAM), the final geometry is produced using layer-by-layer deposition principle of beads. To achieve good geometrical accuracy of the final product, proper implementation of the bead geometry is essential. The process parameters have a direct influence on the bead geometry, thus to the printed part. In this paper, we propose a bead width prediction model to improve deposition accuracy. A regression algorithm is applied to the experimental results to predict the bead width dimension. Bead prediction equation relating the bead width growth for each layer is obtained for a given set of process parameters. The prediction equations show similar evolution trends and confirm the influence of deposition process parameters on the bead width. The proposed method demonstrates a prospective insight on a more proper selection of process or physical parameter intervening in laser wire additive manufacturing process. [less ▲]

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