## 4 Conclusions
### Potentials and Limitations of the Current Study  
The presented ANN is very suitable for usage in additive manufacturing on thin substrates, which has a lot of potential in the applications of incremental manufacturing, for example, for finishing stamped metal sheets with small additive manufactured functional structures. A proper application is used in path planning and minimization of process time for continuous welding processes, as the ANN can continuously update predictions for the required amount of remaining layers and thus welding time. With further utilization of ANNs, an implementation to calculate optimal welding parameters for the desired result with minimal distortion is also possible. However, an extrapolation for more complex components, such as multidimensional, multi-layer structures with complex shape and contour parameters based on these datasets requires further adjustments and training data. With the presented experiments, the groundwork was laid for the usage of enhanced ANNs for wire arc additive manufactured parts and the prediction of their geometries and distortions. Especially for larger components and welded structures that are no longer part of the Incremental Manufacturing process, distortion of the substrate metal plate is often negligible. This is either caused by the usage of very thick plates that possess inherently high stiffness or through the added stiffness of the structure created by the added layers. Disruptive effects still occur in this context, which results in shapes and geometries that can differ significantly from their intended results. This is caused by factors that are difficult to measure or control, such as the heat dissipation in the structure and varying layer height.  
Possible approaches for an adapted and improved, enhanced prediction is the division of components into smaller building blocks of a defined size and layer count. Thereby, the remaining welding work can be approximated after the creation of each of these building blocks. This reduces the need for manual intervention for the welding of near-net shape components and makes a compromise between excellent prediction, which could be achieved by very time-consuming measurements after each welded layer, and realistic processes applicable to industrial setups.  
The used data and scripts used for this study are available at [[https://lnk.tu-bs.de/52f8](https://lnk.tu-bs.de/52f8) kz]([https://lnk.tu-bs.de/52f8](https://lnk.tu-bs.de/52f8) kz) (accessed on 19 May 2021).  
Conceptualization: [PERSON] and [PERSON]; methodology: M.K. and C.W.; software: C.W.; validation: [PERSON], F.A. and J.H.; formal analysis: [PERSON] and M.K.; investigation: [PERSON] and C.W.; resources: [PERSON]. ([PERSON]) and K.D. ([PERSON]); data curation: [PERSON] and M.K.; writing--original draft preparation: [PERSON] and M.K.; writing--review and editing: [PERSON], [PERSON], [PERSON], [PERSON] and K.D. ([PERSON]); visualization: [PERSON] and [PERSON]; supervision: [PERSON] and [PERSON]; project administration: [PERSON], [PERSON], [PERSON] ([PERSON]) and [PERSON] ([PERSON]); funding acquisition: [PERSON], [PERSON], [PERSON] ([PERSON]) and [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Faculty of Mechanical Engineering of the Technische Universitat Braunschweig.  
Not applicable.  
Data Availability Statement: Data supporting the reported results, including code and graphs, can be found at the following link: [[https://lnk.tu-bs.de/52f8](https://lnk.tu-bs.de/52f8) kz]([https://lnk.tu-bs.de/52f8](https://lnk.tu-bs.de/52f8) kz) (accessed on 19 May 2021).  
We acknowledge support by the German Research Foundation and the Open Access Publication Funds of Technische Universitat Braunschweig.  
The authors declare no conflict of interest.