In a repair process chain, damaged areas or cracks can be removed by milling and subsequently be reconditioned with new material deposition. The use of laser metal deposition has been investigated for this purpose. The material has been deposited into different groove shapes, using both stainless steel and Ti-6Al-4 V. The influence of welding parameters on the microstructure and the heat affected zone has been studied. The parameters have been modified in order to achieve low heat input and consequently low distortion as well as low metallurgical impact. Finally, an evaluation of the opportunities for an automatized repair process is made

Maintenance, repair and overhaul of components are of increasing interest for parts of high complexity and expensive manufacturing costs. In this paper a production process for laser metal deposition is presented, and used to repair a gas turbine burner of Inconel 718. Different parameters for defined track geometries were determined to attain a near net shape deposition with consistent build-up rate for changing wall thicknesses over the manufacturing process. Spot diameter, powder feed rate, welding velocity and laser power were changed as main parameters for a different track size. An optimal overlap rate for a constant layer height was used to calculate the best track size for a fitting layer width similar to the part dimension. Deviations in width and height over the whole build-up process were detected and customized build-up strategies for the 3D sequences were designed. The results show the possibility of a near net shape repair by using different track geometries with laser metal deposition

Modern and expensive parts lead to an increasing demand for maintenance, repair and overhaul (MRO) technologies. Instead of part replacement, MRO technologies are economically advantageous throughout the life cycle. Laser metal deposition as modern MRO technology can be used to repair cracks or protect damaged surfaces with a hard facing layer. It is necessary to adjust weld bead profile to the specific task. For this purpose, Design of Experiment (DoE) has a high potential to decrease experimental effort. In this paper, a full factorial design is used to determine the effect of process parameters on the geometric dimensions of the weld bead. The paper is of interest to engineers working with laser metal deposition as well as DoE methods

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 distortion-compensated 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.