Large-scale aluminum parts are used in aerospace and automotive industries, due to excellent strength, light weight, and the good corrosion resistance of the material. Additive manufacturing processes enable both cost and time savings in the context of component manufacturing. Thereby, wire arc additive manufacturing (WAAM) is particularly suitable for the production of large volume parts due to deposition rates in the range of kilograms per hour. Challenges during the manufacturing process of aluminum alloys, such as porosity or poor mechanical properties, can be overcome by using arc technologies with adaptable energy input. In this study, WAAM of AlMg5Mn alloy was systematically investigated by using the gas metal arc welding (GMAW) process. Herein, correlations between the energy input and the resulting temperature–time-regimes show the effect on resulting microstructure, weld seam irregularities and the mechanical properties of additively manufactured aluminum parts. Therefore, multilayer walls were built layer wise using the cold metal transfer (CMT) process including conventional CMT, CMT advanced and CMT pulse advanced arc modes. These processing strategies were analyzed by means of energy input, whereby the geometrical features of the layers could be controlled as well as the porosity to area portion to below 1% in the WAAM parts. Furthermore, the investigations show the that mechanical properties like tensile strength and material hardness can be adapted throughout the energy input per unit length significantly.

Wire arc additive manufacturing (WAAM) by gas metal arc welding (GMAW) is a suitable option for the production of large volume metal parts. The main challenge is the high and periodic heat input of the arc on the generated layers, which directly affects geometrical features of the layers such as height and width as well as metallurgical properties such as grain size, solidification or material hardness. Therefore, processing with reduced energy input is necessary. This can be implemented with short arc welding regimes and respectively energy reduced welding processes. A highly efficient strategy for further energy reduction is the adjustment of contact tube to work piece distance (CTWD) during the welding process. Based on the current controlled GMAW process an increase of CTWD leads to a reduction of the welding current due to increased resistivity in the extended electrode and constant voltage of the power source. This study shows the results of systematically adjusted CTWD during WAAM of low-alloyed steel. Thereby, an energy reduction of up to 40% could be implemented leading to an adaptation of geometrical and microstructural features of additively manufactured work pieces.

As part of a feasibility study, an alternative production process for titanium aluminides was investigated. This process is based on in situ alloying by means of a multi-wire technique in the layer-wise additive manufacturing process. Thereby, gas metal arc welding (GMAW) was combined with additional hot-wire feeding. By using two separate wires made of titanium and aluminum, it is possible to implement the alloy formation of titanium aluminides directly in the weld bead of the welding process. In this study, wall structures were built layer-by-layer with alloy compositions between 10 at% and 55 at% aluminum by changing the feeding rates. During this investigation, the macroscopic characteristics, microstructural formation, and the change of the microhardness values were analyzed. A close examination of the influence of welding speed and post-process heat treatment on the Ti–47Al alloy was performed; this being particularly relevant due to its economically wide spread applications.

This research presents a hybrid approach to generate sample data for future machine learning applications for the prediction of mechanical properties in directed energy deposition-arc (DED-Arc) using the GMAW process. DED-Arc is an additive manufacturing process which offers a cost-effective way to generate 3D metal parts, due to its high deposition rate of up to 8 kg/h. The mechanical properties additively manufactured wall structures made of the filler material G4Si1 (ER70 S-6) are shown in dependency of the t_8/5 cooling time. The numerical simulation is used to link the process parameters and geometrical features to a specific t_8/5 cooling time. With an input of average welding power, welding speed and geometrical features such as wall thickness, layer height and heat source size a specific temperature field can be calculated for each iteration in the simulated welding process. This novel approach allows to generate large, artificial data sets as training data for machine learning methods by combining experimental results to generate a regression equation based on the experimentally measured t_8/5 cooling time. Therefore, using the regression equations in combination with numerically calculated t_8/5 cooling times an accurate prediction of the mechanical properties was possible in this research with an error of only 2.6%. Thus, a small set of experimentally generated data set allows to achieve regression equations which enable a precise prediction of mechanical properties. Moreover, the validated numerical welding simulation model was suitable to achieve an accurate calculation of the t_8/5 cooling time, with an error of only 0.3%.