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- 2020 (4) (entfernen)
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- Additive Manufacturing (1)
- Additive manufacturing (1)
- Artificial neural network (1)
- Auftragschweißen (1)
- Clustering (1)
- Computer vision (1)
- DED (1)
- Deep learning (1)
- Dimensional accuracy (1)
- Directed Energy Deposition (1)
Organisationseinheit der BAM
Directed energy deposition additive manufactured parts have steep stress gradients and an anisotropic microstructure caused by the rapid thermo-cycles and the layer-upon-layer manufacturing, hence heat treatment can be used to reduce the residual stresses and to restore the microstructure. The numerical simulation is a suitable tool to determine the parameters of the heat treatment process and to reduce the necessary application efforts. The heat treatment simulation calculates the distortion and residual stresses during the process. Validation experiments are necessary to verify the simulation results. This paper presents a 3D coupled thermo-mechanical model of the heat treatment of additive components. A distortion-based validation is conducted to verify the simulation results, using a C-ring shaped specimen geometry. Therefore, the C-ring samples were 3D scanned using a structured light 3D scanner to compare the distortion of the samples with different post-processing histories.
Dieser Beitrag stellt Ergebnisse der Untersuchungen zum Auftragschweißen als Plasma-Laserstrahl-Hybrid-Prozess dar. Es hat sich gezeigt, dass ein Laserstrahl, der in einer gemeinsamen Prozesszone mit einem Plasma-Pulver-Auftragschweißprozess vorlaufend angeordnet ist, diesem Prozess eine erhebliche Geschwindigkeitssteigerung sowie eine Verbesserung der Stabilität ermöglicht. Der Hybrid-Prozess konnte mit Verschleißschutzwerkstoffen
sowie dem Korrosionsschutzwerkstoff Inconel 625 bei Vorschubgeschwindigkeiten von bis zu 10 m/min erfolgreich validiert werden.
Im Hinblick auf die aktuellen Entwicklungen zu Hochgeschwindiglceits-Laserstrahlauftragschweißen kann der Plasma-Laserstrahl-Hybrid-Prozess
zwischen diesen und den konventionellen Verfahren eingeordnet werden.
Components distort during directed energy deposition (DED) additive manufacturing (AM) due to the repeated localised heating. Changing the geometry in such a way that distortion causes it to assume the desired shape – a technique called distortion-compensation – is a promising method to reach geometrically accurate parts. Transient numerical simulation can be used to generate the compensated geometries and severely reduce the amount of necessary experimental trials. This publication demonstrates the simulation-based generation of a distortioncompensated DED build for an industrial-scale component. A transient thermo-mechanical approach is extended for large parts and the accuracy is demonstrated against 3d-scans. The calculated distortions are inverted to derive the compensated geometry and the distortions after a single compensation iteration are reduced by over 65%.
This paper demonstrates that the instrumented indentation test (IIT), together with a trained artificial neural network (ANN), has the capability to characterize the mechanical properties of the local parts of a welded steel structure such as a weld nugget or heat affected zone.
Aside from force-indentation depth curves generated from the IIT, the profile of the indented surface deformed after the indentation test also has a strong correlation with the materials’ plastic behavior. The profile of the indented surface was used as the training dataset to design an ANN to determine the material parameters of the welded zones. The deformation of the indented surface in three dimensions shown in images were analyzed with the computer vision algorithms and the obtained data were employed to train the ANN for the characterization of the mechanical properties. Moreover, this method was applied to the images taken with a simple light microscope from the surface of a specimen. Therefore, it is possible to quantify the mechanical properties of the automotive steels with the four independent methods: (1) force-indentation depth curve; (2) profile of the indented surface; (3) analyzing of the 3D-measurement image; and (4) evaluation of the images taken by a simple light microscope. The results show that there is a very good Agreement between the material parameters obtained from the trained ANN and the experimental uniaxial tensile test. The results present that the mechanical properties of an unknown steel can be determined by only analyzing the images taken from its surface after pushing a simple indenter into its surface.