## 3 Results and Discussion
### Optimization Results  
Figure 5a-d presents the data obtained from 247 simulations conducted by varying scanning speed, spot radius, layer thickness (50 \(\upmu\)m), and laser power. Within this analysis, critical parameters such as flow surface area, melt depth, melt volume, micro-porosity percentage, and laser absorption level (80%) were examined in detail. The simulation software represents dimensions such as track width as radius in graphical outputs. However, in this study, descriptions are provided in terms of diameter. This is because the input parameters for the SLM machine are defined in diameter. In SLM simulations (Figure 5a), reducing the scanning speed increases the interaction time between the laser and the powder bed, leading to a higher energy input per unit length. This results in the formation of a larger melt pool surface area. As the applied laser power increases, this effect becomes more pronounced, causing more material to melt. On the other hand, increasing the laser spot diameter distributes energy over a wider area, reducing energy density and thereby limiting the expansion of the melt pool surface area. The maximum fluid surface area is observed under conditions of low scanning speed, high laser power, and a small spot diameter. In LPBF simulations (Figure 5b), a decrease in scan speed increases the interaction time between the laser beam and the powder bed, leading to a higher energy input per unit length. This increased energy input results in the formation of a deeper melt pool, which in turn causes an increase in melt pool thickness. This effect becomes more pronounced with higher laser powers, which provide additional thermal energy. On the other hand, an increase in laser spot diameter causes the energy to spread over a wider area, reducing energy density and potentially leading to a larger but shallower melt pool. Therefore, under conditions of low scan speed, high laser power, and small spot diameter, the melt pool thickness reaches its maximum, while the opposite conditions result in the formation of thinner melt layers. In LPBF simulations (Figure 5c), reducing the scan speed increases the energy input per unit length, leading to a larger amount of material melting. This effect becomes more pronounced with higher laser power, which provides additional thermal energy, resulting in deeper and wider melt pools. However, increasing the laser spot diameter causes the laser energy to spread over a larger area, reducing energy density, which may limit the melt volume. Therefore, the maximum melt volume is achieved under conditions of low scan speed, high laser power, and small spot diameter. In LPBF simulations (Figure 5d), reducing the scan speed increases the energy input per unit length, which may promote full melting and contribute to a reduction in micro porosity. However, when excessively low scan speeds combine with high laser power, vaporization and keyhole instabilities can occur, leading to gas entrapment and an increase in porosity. Larger laser spot diameters reduce energy density, resulting in insufficient melting and consequently higher micro porosity formation. In contrast, smaller spot diameters focus the energy more intensively, enhancing melt homogeneity and reducing porosity. Therefore, minimizing micro porosity requires an optimized combination of scan speed, laser power, and spot diameter. When multiple objectives are defined in the optimization process, the interactions between parameters must be carefully evaluated. As a result of the analysis, the optimal parameter combination was found in design 29. This design provides significant improvements in surface area and micro-porosity; although it does not achieve the targeted value for melt pool depth, it was determined to be the most efficient design overall. These results demonstrate that the careful selection of process parameters directly affects the final material properties in additive manufacturing processes.