## 1 Introduction
Considering the difficulties in bringing topology optimization based designs into reality with conventional manufacturing methodologies, one naturally agrees to the idea of employing AM for their realizations [11]. [PERSON] et al. [12] carried out a simple C-clip component using classical topology approach (TA), lattice approach (LA), and the hybrid approach as a smart mix of the TA and LA considering printing time as the process constraint in order to create a highly complex truss structure and evaluate which of the advantages are offered by their integration. On the other hand, the interest and attentions among researchers on the applicability of AM in remanufacturing has increased. However, there is a lack of systematic approaches based on artificial intelligence for optimization purposes [13]. Artificial neural networks and optimization algorithms are applied as surrogate models to predict the hardpoints as the surrogate-based optimization method plays an essential role in the optimization processes, especially when the optimization model is established based on computational modelling complexities and uncertainties [14]. [PERSON] et al. [15] overviewed ANN approaches in terms of application area in AM whole chain. They pointed out that process parameters for DED methods such as LMD and WAAM are laser power, scanning speed, powder feeding rate, bead width, bead height, and centre distance of adjacent deposition paths. [PERSON] et al. [16] applied ANN to define the classification of melt-pool images with different heat source power for selective laser melting which is the one of PBF tech nologies. It was seen that the classification model could be utilized to infer the location to cause the unexpected alteration of microstructures or separate the defective products non-destructively. [PERSON] et al. [17] used ANN in order to predict the geometric deviations for fused deposition modelling, classifying the process images as well. [PERSON] et al. [18] presented a framework for the PBF process parameters optimization by mapping the ultimate quality of a fabricated part into the process parameters and material properties. The effects of laser power and scan speed on the microstructure and porosity of SS 316L fabricated parts were investigated in their first phase studies. They carried out an ANN application in order to suggest the optimized process parameters according to the desired ultimate quality of an ordered part manufactured via PBF method. [PERSON] et al. [19] optimized the process parameters to obtain microstructure without porosity and good interfacial bonding between the substrate and the deposition. [PERSON]'s L9 method was used for optimization experiments and the explored ranges of powder feed rate, laser power, and laser scan speed. The microstructure and microhardness of the built specimens were studied. The objective of this work is to examine the potentiality offered by the integration of the two structures (plain metal and DED structures) to improve the final part property such as the specific energy absorption considering the optimum peak force and total mass constraints within mapping the process parameters on the ultimate properties of to-be-fabricated parts. This will help to fill the existing gap for development of the DED hybrid product, which includes the contributing and controllable design and process parameters. This research conducts a product and process optimization study of a simple test case geometry (semi cross large metal component), where structural topology optimization has been applied using an artificial neural network and genetic algorithms based on the design of feasible DED bead structure geometries and heat source power. A computational efficient approach is developed for constructing a surrogate model for problems where both inputs and outputs are high-dimensional and nonlinear using small training data set.