## 4 Conclusions  
This study has shown that a lower t\({}_{8/5}\) cooling time is beneficial to the mechanical properties of additively manufactured structures made of G4 Si1. In DED-Arc it is not always possible to ensure the same cooling times for the whole additively manufactured structure due to geometrical constraints, such as a irregular wall thickness, curves, edges and cross points in a three-dimensional freeform shape. Thus, it is key to approximate the mechanical properties based on different t\({}_{8/5}\) cooling times; moreover, this enables machine learning algorithms to obtain a much larger amount of data due to the generation of synthetic data. Therefore, the underlying regression equations of the relation between mechanical property and t\({}_{8/5}\) cooling time are needed. The hardness and t\({}_{8/5}\) cooling times in the state of the art demonstrate the logarithmic relation between them. The results found in this research can be concluded with the following statements:  
* The hardness, tensile strength, yield strength and elongation at break show a logarithmic relation with the t\({}_{8/5}\) cooling times (Figures 9 and 10).
* Therefore, it is possible to calculate values between the given data points by using the regression equations Figures 9 and 10.
* The impact work was the only mechanical property tested in this research with no obvious relation to the t\({}_{8/5}\) cooling times (Figure 11).
* The numerical simulation was a suitable tool to calculate the t\({}_{8/5}\) cooling time (Figure 13) for a tested parameter set (Figure 15) with a deviation of only 0.3% (Table 4).
* The regression equations have proven to have a high prediction accuracy, with only small deviations from the measured values of 1.2% for the hardness, 1.1% for the tensile strength, 0.7% for the yield strength and 2.6% for the elongation at break (Table 4).  
Yet, a completely validated simulation model for different parameter sets would provide even more accurate results. Therefore, a combination of the numerical simulation and the experimentally determined regression equations can be used as a tool for the data augmentation. As the given parameter sets were limited in this research, a broader study would generate more accurate regression equations. In future, a more complete data set should be used as the basis for regression equations. Parameter sets with higher cooling times than 35.1 s and lower than 8 s would be needed.  
Conceptualization, J.R.; methodology, J.R., S.H., P.H., Y.A. and M.R.; software, J.R. and M.R.; validation, J.R.; investigation, J.R. and A.R.; data curation, J.R.; writing--original draft preparation, J.R.; writing--review and editing, J.R.; visualization, J.R. and M.R.; supervision, J.P.B. and J.H.; project administration, J.P.B. and J.H. [PERSON] is working at Production Technology Group, Technische Universitat Ilmenau as all the other authors. All authors have read and agreed to the published version of the manuscript.  
This article is based on results obtained during a research project funded by the \"Zukunft Bau\" research initiative of the Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR) (AZ: SWD-10.08.18.7-17.46). The authors are responsible for the content of the publication.  
Not applicable.  
We would like to thank EWM@ AG for providing the welding equipment and Westfalische Drahindustrie GmbH for providing the filler material. We acknowledge support for the publication costs by the Open Access Publication Fund of the Technische Universitat Ilmenau.  
The authors declare no conflict of interest.