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Wire arc additive manufacturing enables the production of near-net shape large-volume metallic components leveraging an established industrial base of welding and cladding technology and adapting it for layer-wise material deposition. However, the complex relationship between the process parameters and resulting mechanical properties of the components still remains challenging. In case of high-strength Al-Mg-Si aluminum alloys, no commercial filler wires are yet available due the high susceptibility of solidification cracking as well as the necessary efforts to obtain acceptable mechanical properties. To address this need, we evaluated a novel filler wire based on AlMg0.7Si doped with a Ti5B1 master alloy to foster fine equiaxed grains within the deposited metal. The correlation between the process parameters and component quality was examined by analyzing the size and distribution of pores as well as the grain morphology. Furthermore, we evaluated the influence of different post-weld heat treatment strategies to achieve mechanical properties corresponding to the reference wrought material. We demonstrated that fine equiaxed grains in the weld metal reduced the susceptibility of solidification cracking significantly. The novel AlMg0.7Si-TiB (S Al 6063-TiB) filler wire facilitated wire arc additive manufacturing of high-strength aluminum components with mechanical properties that were almost as superior as the corresponding wrought base material.
The application of magnesium (Mg) inevitably involves dissimilar welding with steel. A novel solid state spot welding method, refill friction stir spot welding (refill FSSW), was utilized to weld AZ31 Mg alloy to galvanized DP600 steel. Although Mg/Fe is an immiscible alloy system, defect-free welds with high strength were successfully obtained in a wide parameter window. The results of microstructure, interfacial reactions, and mechanical properties are reported to reveal the underlying joining mechanism. Due to the melting of Zn coating and subsequent Mg-Zn reactions, Mg-Zn eutectic and intermetallic compounds were detected within welds. Heterogeneous interfacial reactions occur along Mg/steel interface, and the relationship between interfacial structure and fracture behavior was investigated. The joining mechanism is associated with Zn coating and Fe-Al layer: 1) the presence of Zn coating is beneficial for achieving high-quality welding between Mg and steel, it protects the interface from oxidation and contributes to brazing of the weld; 2) the Al present in Mg alloy reacts with Fe, resulting in the growth of Fe-Al layer, which contributes to the diffusion bonding in the interface. The overall results clearly show that Refill FSSW is a competitive welding method for joining Mg and galvanized steel.
In additive manufacturing (AM) directed energy deposition (DED), parts are built by welding layers of powder or wire feedstock onto a substrate with applications for steel powders in the fields of forging tools, spare parts, and structural components for various industries. For large and bulky parts, the choice of toolpaths influences the build rate, the mechanical performance, and the distortions in a highly geometry-dependent manner. With weld-path lengths in the range of hundreds of meters, a reliable, automated tool-path generation is essential for the usability of DED processes. This contribution presents automated tool-path generation approaches and discusses the results for arbitrary geometries. Socalled “zig-zag” and “contour-parallel” processing strategies are investigated and the tool-paths are automatically formatted into machine-readable g-code for experimental validation to build sample geometries. The results are discussed in regard to volume-fill, microstructure, and porosity in dependence of the path planning according to photographs and metallographic cross-sections.
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.