###### Abstract  
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. WAAM; ANN; multi-layer; machine learning; distortion; welding +
Footnote †: journal: Medicalincremental manufacturing (IM) [5; 6; 7]. IM is a production concept where base parts are produced in high volume and then incrementally finalized with different processes such as additive manufacturing and machining [8]. For an efficient manufacturing process, techniques with high deposition rates such as WAAM are considered advantageous for this manufacturing concept. For the combination of WAAM and IM, the concept consists of finalizing a large number of prefabricated metal parts with build-up welding and thus resulting in the required structures and geometry. However, one of the major challenges during WAAM is the occurrence of welding distortion due to a comparatively high heat input and the related inhomogeneous plastic strains [9], leading to the formation of residual stress as well as distortion of the component. Distortion affects, next to the final part geometry, also the manufacturing process itself. Component warping may lead to unstable process behavior or even process abortion. Hence, the distortion needs to be considered either during offline process planning or in-situ by adaptive manufacturing strategies. Since the manufacturing process is complex and distortion is influenced by many parameters such as energy input, temperature regimes, part geometry, inner and outer restraint, and material parameters, an enhanced prediction method is needed. In order to account for these influencing factors in predictions of the AM process results, several approaches have been implemented. Simulations with thermal finite elements (FE) and other thermal modeling such as thermo-elastic-plastic transient models [10; 11], GAMMA-simulation [12] as well as fuzzy logic [13] have been used for welding processes. Due to the difficulty and the often very limited available computational times required for accurate simulations for welding processes, the usage of artificial neural networks (ANN) has been applied to these multi-criteria problems to a much larger extent, often in combination with simulations [11; 12; 13], although the use cases differ. Several researchers examine the influence of specific parameters such as welding gases [14; 15] with ANNs, often with a focus on the ANN-based prediction of the resulting welding bead geometries. These are mostly based on the usage of welding parameters such as layer count, welding speed and current as well as voltage as inputs with bead width and height as outputs. Variations in the usage of these parameters can be found. [PERSON] et al. only consider the bead height as output, [PERSON] et al.