An increasing demand for flexibility and product integration, combined with reduced product development cycles, leads to continuous development of new manufacturing technologies such as additive manufacturing. Wire and arc additive manufacturing (WAAM) provides promising technology for the near net-shape production of large structures with complex geometry, using cost efficient production resources such as arc welding technology and wire materials. Compared to powder-based additive manufacturing processes, WAAM offers high deposition rates as well as enhanced material utilization. Because of the layer-by-layer built up approach, process conditions such as energy input, arc characteristics, and material composition result in a different processability during the additive manufacturing process. This experimental study aims to describe the effects of the welding process on buildup accuracy and material properties during wire arc additive manufacturing of aluminum structures. Following a process development using pulse cold metal transfer (CMT-P), linear wall samples were manufactured with variations of the filler metal. The samples were analyzed in terms of surface finishing, hardness, and residual stress. Furthermore, mechanical properties were determined in different building directions.

Additive manufacturing (AM) in industrial applications benefits from increasing interest due to its automation potential and its flexibility in manufacturing complex structures. The construction and architecture sector sees the potential of AM especially in the free form design of steel components, such as force flow optimized nodes or bionic-inspired spaceframes. Robot-guided wire and arc additive manufacturing (WAAM) is capable of combining a high degree of automation and geometric freedom with high process efficiency. The build-up strategy (layer by layer) and the corresponding heat input influence the mechanical properties of the WAAM products. This study investigates the WAAM process by welding a bar regarding the build-up geometry, surface topography, and material properties. For tensile testing, an advanced testing procedure is applied to determine the strain fields and mechanical properties of the bars on the component and material scale.

Wire arc additive manufacturing (WAAM) is a direct energy deposition (DED) process with high deposition rates, but deformation and distortion can occur due to the high energy input and resulting strains. Despite great efforts, the prediction of distortion and resulting geometry in additive manufacturing processes using WAAM remains challenging. In this work, an artificial neural network (ANN) is established to predict welding distortion and geometric accuracy for multilayer WAAM structures. For demonstration purposes, the ANN creation process is presented on a smaller scale for multilayer beads on plate welds on a thin substrate sheet. Multiple concepts for the creation of ANNs and the handling of outliers are developed, implemented, and compared. Good results have been achieved by applying an enhanced ANN using deformation and geometry from the previously deposited layer. With further adaptions to this method, a prediction of additive welded structures, geometries, and shapes in defined segments is conceivable, which would enable a multitude of applications for ANNs in the WAAM-Process, especially for applications closer to industrial use cases. It would be feasible to use them as preparatory measures for multi-segmented structures as well as an application during the welding process to continuously adapt parameters for a higher resulting component quality.