## 3 Artificial Neural Networks and Optimization
#### Artificial Neural Network Application
ANN models can predict the effects of the different variables, such as the DED bead height, width, and distance, base material thickness, pattern type, and heat source power on the performance of a cross member part with an acceptable and good accuracy to predict the inputs and outputs. The proposed ANN + GA model offers a surrogate model with higher prediction ability than DOE RSM method for energy and total mass. On the other hand, the DOE RSM approach gives the closed prediction for peak force \begin{table}
\begin{tabular}{c c c c} \hline \hline
**DBW** & **DBH** & **DBD** & **BMT** & **PP** & **LP/HS** \\ \hline
4.3542 & 5.8669 & 45 & 1.6724 & 1 & 1342.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: ANN + GA optimization results of design and process parameters. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **DOE RSM** & **ANN + GA** & **\% Improvement or Deterioration** \\  & **Error** & **Error** & **with ANN + GA** \\ \hline FORCE (kN) & 11.75\% & 18.86\% & \(-\)60.51 \\ ENERGY (Joule) & \(-\)26.51\% & \(-\)11.72\% & \(+\)55.79 \\ TOTAL MASS (kg) & 21.46\% & 0.75\% & \(+\)96.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: ANN prediction of force, energy dissipation, and total mass. values. This study demonstrates that the ANN-based optimization approach can be used in design optimization studies on the hybrid products manufactured via the DED process. In this study, a locally reinforced semi cross part is manufactured using the proposed approach. As a result of the physical test on the bench after hybrid DED cross prototypes, it has been found that locally reinforced cross beam has much more energy absorption and peak force values than plain model, with a performance increase of over 65%. It is seen that the proposed NN-GA is a promising approach to generate the topology based geometrical patterns and process parameters which can be used to create a local reinforced product as hybrid manufacturing technologies. The presented techniques in this study have some limits. In the case of other alloys, such as titanium alloys, the error may increase. For the same specific energy density, more material melts for titanium based alloys in comparison with iron-based alloys. It may be necessary to redefine the heat source model according to melt pool shapes and obtain the flow curves as suitable in order to correlate CAE model with the physical results. Conceptualization, M.C., E.I.A. and F.O. ; methodology, M.C., E.I.A. and F.O. ; software, M.C. ; validation, M.C. ; formal analysis, M.C. ; investigation, M.C. ; resources, M.C., E.I.A. and F.O. ; data curation, M.C. ; writing--original draft preparation, M.C. ; writing--review and editing, M.C., E.I.A. and F.O. ; visualization, M.C. ; supervision, F.O. ; project administration, M.C. and F.O. ; funding acquisition, M.C. All authors have read and agreed to the published version of the manuscript. This work was supported by Tubitak and Coskunoz Holding [2244 Industrial Doctorate Programme No: 119C059]; and by Tubitak, Eureka, Eureka Smart Cluster and Coskunoz Holding [grant agreement number 9190007]. The authors would like to express their sincere gratitude to Tubitak and Coskunoz Holding which offers its software and hardware infrastructure. The authors declare that there are no conflicts of interest to disclose. ## Nomenclature

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