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Motivated by the tensile strength loss of 9%Ni steel arc welded joints made using Ni-based austenitic filler metals, the feasibility of maintaining the tensile strength using matching ferritic filler metal has been demonstrated. In comparison with shielded metal arc welded joint made using Ni-based austenitic electrode ENiCrMo-6, higher tensile strength comparable to that of the base metal was obtained using matching ferritic electrode. Besides, sufficient impact toughness energies with much lower mismatch were obtained for weld metal and heat-affected zone.
Welded joint with a lower mechanical mismatching is of considerable importance for achieving acceptable combination of tensile strength and impact toughness. A better combination of These mechanical properties is ensured by applying a post weld heat treatment.
Motivated by the tensile strength loss of 9%Ni steel arc welded joints made using Ni-based austenitic filler metals, the feasibility of maintaining the tensile strength using matching ferritic filler metal has been demonstrated. In comparison with shielded metal arc welded joint made using Ni-based austenitic electrode ENiCrMo-6, higher tensile strength comparable to that of the base metal was obtained using matching ferritic electrode. Besides, sufficient impact toughness energies with much lower mismatch were obtained for weld metal and heat-affected zone.
Welded joint with a lower mechanical mismatching is of considerable importance for achieving acceptable combination of tensile strength and impact toughness.Abetter combination of These mechanical properties is ensured by applying a post weld heat treatment.
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.
AlSi10Mg is one of the most applied alloys for laser powder bed fusion (LPBF) technology, due to its great possibilities for implementing new lightweight concepts such as in automotive industries. For the component design it is necessary to know about the mechanical properties and the mechanical behaviour. The many published strength properties of LPBF processed AlSi10Mg show significant differences up to approximately 225 MPa in ultimate tensile strength (UTS) and 195 MPa in yield strength (YS). To understand these varying properties, a ring trial was carried out manufacturing specimens on 6 LPBF machines with different parameters and build-up strategies. They were studied in the as-built (AB) condition and after heat treatment at 300 °C for 30 min, respectively. For examining the mechanical properties, tensile tests and hardness measurements were carried out. The microstructure was characterized by optical light microscopy (OM), field emission scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD) and electron backscatter diffraction (EBSD). The identified differences in strength properties were discussed based on the 4 strengthening mechanism known for metallic materials and at the background of material defects. It was found that the size of the typical sub-cell structure of LPBF AlSi10Mg affected substantially the mechanical properties in the AB condition, in which with decreasing sub-cell size strength increased. If heat treatment was applied, the strength properties decreased and did not differ anymore. Since annealing led to coarsened sub-cells, whereas the grains itself did not change in size, the influence of sub-cell structure on strength was further confirmed. In addition, acicular precipitates in the AB condition were observed at specimens from one LPBF machine showing the lowest tensile elongation.
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.