Ni-based superalloys are well established in various industrial applications, because of their excellent mechanical properties and corrosion resistance at high temperatures. Despite the high development stage and a common industrial use of these alloys, hot cracking remains a major challenge limiting the weldability of the materials. As commonly known, the hot cracking susceptibility during welding increases with the amount of precipitation phases. Hence, a large amount of highstrength Ni-Alloys is rated as non-weldable. A new approach based on electron beam welding at low feed rates shows great potential for reducing the hot cracking tendency of precipitation-hardened alloys. However, geometry and properties of the weld seam differ significantly in comparison to the common process range for practical uses. The aim of this study is to investigate the influence of welding parameters on the seam geometry at low feed rates between 1 mm/s and 10 mm/s. For this purpose, 25 bead on plate welds on a 12 mm thick sheet made of Inconel 718 are carried out. First, the relevant parameters are identified by performing a screening. Then the effects discovered are further studied by using a central composite design. The results show a significant difference between the analyzed weld seam geometry in comparison to the well-known appearance of electron beam welded seams.

The Directed Energy Deposition process is used in a wide range of applications including the repair, coating or modification of existing structures and the additive manufacturing of individual parts. As the process is frequently applied in the aerospace industry, the requirements for quality assurance are extremely high. Therefore, more and more sensor systems are being implemented for process monitoring. To evaluate the generated data, suitable methods must be developed. A solution, in this context, was the application of artificial neural networks (ANNs). This article demonstrates how measurement data can be used as input data for ANNs. The measurement data were generated using a pyrometer, an emission spectrometer, a camera (Charge-Coupled Device) and a laser scanner. First, a concept for the extraction of relevant features from dynamic measurement data series was presented. The developed method was then applied to generate a data set for the quality prediction of various geometries, including weld beads, coatings and cubes. The results were compared to ANNs trained with process parameters such as laser power, scan speed and powder mass flow. It was shown that the use of measurement data provides additional value. Neural networks trained with measurement data achieve significantly higher prediction accuracy, especially for more complex geometries.

Pores in additive manufactured metal parts occur due to different reasons and affect the part quality negatively. Few investigations on the origins of porosity are available, especially for Ni-based super alloys. This paper presents a new study to examine the influence of common processing parameters on the formation of pores in parts built by laser metal deposition using Inconel 718 powder. Further, a comparison between the computed tomography (CT) and the Archimedes method was made. The investigation shows that CT is able to identify different kinds of pores and to give further information about their distribution. The identification of some pores as well as their shape can be dependent on the parameter setting of the analysis tool. Due to limited measurement resolution, CT is not able to identify correctly pores with diameters smaller than 0.1 mm, which leads to a false decrease in overall porosity. The applied Archimedes method is unable to differentiate between gas porosity and other kinds of holes like internal cracks or lack of fusion, but it delivered a proper value for overall porosity. The method was able to provide suitable data for the statistical evaluation with design of experiments, which revealed significant parameters on the formation of pores in LMD.

Directed energy deposition (DED) has been in industrial use as a coating process for many years. Modern applications include the repair of existing components and additive manufacturing. The main advantages of DED are high deposition rates and low energy input. However, the process is influenced by a variety of parameters affecting the component quality. Artificial neural networks (ANNs) offer the possibility of mapping complex processes such as DED. They can serve as a tool for predicting optimal process parameters and quality characteristics. Previous research only refers to weld beads: a transferability to additively manufactured three-dimensional components has not been investigated. In the context of this work, an ANN is generated based on 86 weld beads. Quality categories (poor, medium, and good) are chosen as target variables to combine several quality features. The applicability of this categorization compared to conventional characteristics is discussed in detail. The ANN predicts the quality category of weld beads with an average accuracy of 81.5%. Two randomly generated parameter sets predicted as “good” by the network are then used to build tracks, coatings, walls, and cubes. It is shown that ANN trained with weld beads are suitable for complex parameter predictions in a limited way.

In recent years, in addition to the commonly known wire-based processes of Directed Energy Deposition using lasers, a process variant using the electron beam has also developed to industrial market maturity. The process variant offers particular potential for processing highly conductive, reflective or oxidation-prone materials. However, for industrial usage, there is a lack of comprehensive data on performance, limitations and possible applications. The present study bridges the gap using the example of the high-strength aluminum bronze CuAl8Ni6. Multi-stage test welds are used to determine the limitations of the process and to draw conclusions about the suitability of the parameters for additive manufacturing. For this purpose, optimal ranges for energy input, possible welding speeds and the scalability of the process were investigated. Finally, additive test specimens in the form of cylinders and walls are produced, and the hardness profile, microstructure and mechanical properties are investigated. It is found that the material CuAl8Ni6 can be well processed using wire electron beam additive manufacturing. The microstructure is similar to a cast structure, the hardness profile over the height of the specimens is constant, and the tensile strength and elongation at fracture values achieved the specification of the raw material.