## 3 Results and Discussion
### Optimization Results
The HEEDS optimization algorithm, based on the conducted analyses, identified the optimal 14 parameter combinations, which are presented in Figure 6. During the optimization process, process parameters that minimize porosity while enabling the formation of a stable and homogeneous melt pool were determined. The simulations conducted allowed for a detailed analysis of the effects of parameters such as laser power, scan speed, laser spot diameter, and layer thickness on melt pool formation. Figure 5: _Cont._  
The optimized process parameters create a stable melt pool (achieving both a balanced depth-to-width ratio and enabling a homogeneous temperature distribution), thereby minimizing porosity and enhancing the structural integrity of the component. As a result, higher mechanical strength, improved fatigue life, and more predictable quality control during the manufacturing process are achieved. In this context, careful selection of laser parameters ensures that the production process progresses efficiently and without errors. The detailed comparison of the 14 best-performing designs revealed that Design 29 achieved a 38% higher melt pool volume and a 42% reduction in micro-porosity compared to the baseline. Specifically, when laser power was set at 350 W, scanning speed at 120 mm/s, spot diameter at 50 um, and layer thickness at 50 um, the resulting melt pool volume was 5.8 \(\times\) 10\({}^{-3}\) mm\({}^{3}\), and the porosity dropped below 0.8%. These quantitative results confirm that optimal combinations not only improve melt pool stability but also directly influence the structural density and mechanical performance of the part. Figure 5: (**a–d**) Results obtained from 247 simulations conducted with a 10 μm mesh size. Simulations conducted during the optimization process resulted in up to 80% laser absorption, a 60% reduction in micro-porosity, and over a 30% improvement in melt pool volume and surface area. By detailing the effects of laser parameters on melt pool size, cooling rates, and solidification mechanisms, the simulations underscored the critical importance of process control. These findings demonstrate that optimizing process parameters in additive manufacturing significantly influences both microstructural and macrostructural properties. With the right parameter combinations, it is possible to control the manufacturing process to produce more durable, defect-free, and high-performance components. Furthermore, the results emphasize the significance of optimizing single-track formation in SLM processes. However, to gain a more comprehensive understanding of the laser melting behavior, future studies are encouraged to focus on the following areas:  
* Investigation of different material alloys;
* Experimental validation: Conducting tests with the same parameters in actual production environments will enhance the reliability of the simulation results;
* Integration of machine learning: Employing machine learning algorithms to analyze large datasets can expedite the identification of optimal parameter combinations;
* Impact of single-track formation on microstructure: Future research should explore how single-track formation affects microstructural evolution. In the context of accelerating industrial-scale production, improving quality standards, and reducing manufacturing costs, the optimization of single-track formation is expected to become increasingly vital in future research efforts.