Wissenschaftliche Artikel der BAM
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- 2020 (3) (entfernen)
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- Mechanical properties (2)
- Additive manufacturing (1)
- Artificial neural network (1)
- Automated manufacturing (1)
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- Computer vision (1)
- Deep learning (1)
- Fatigue tests (1)
- High strength steels (1)
- Instumented indentation test (1)
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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.
The development within the offshore wind sector towards more powerful turbines combined with increasing water depth for new wind parks is challenging both the designer as well as the manufacturer of bottom fixed support structures. Besides XL-monopiles, the market developed an innovative and economic jacket support structure which is based on automatically manufactured tubular joints combined with standardized pipes. Besides the improvements for a serial manufacturing process the automatically welded tubular joints show a great potential in terms of fatigue resistance e.g. due to a smooth weld geometry without sharp notches. However, these benefits are not considered yet within the fatigue design process of automatically manufactured jacket substructures according to current standards due to the lack of suitable S-N curves. Therefore, 32 axial fatigue tests on single and double-sided automatically welded tubular X-joints have been performed to determine a new hot spot stress related S-N curve. Based on these constant amplitude fatigue tests a new S-N curve equal to a FAT 126 curve was computed which implicitly includes the benefits of the automatically welding procedure.
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.