## 5 Results and Discussion
In the first iteration, the average safety factor (\(\upmu\)SF) and its standard deviation (\(\upsigma\)SF) were 0.947 and 0.645, respectively. No significant performance change was observed in the second and third iterations. However, a notable increase in the average safety factors of the designs was seen after the fourth and fifth iterations. Compared to the first iteration, there was a slightdecrease of 0.01% in the safety factor at the end of the second iteration. In subsequent iterations, the average mechanical performance characteristics of the generated designs showed increases of 3.8%, 11.2%, 16.6%, and 20.3%, respectively. The findings revealed that there was a statistically significant improvement in the mechanical properties of 3D models generated with the revised dataset. In addition, the included 3D models, produced with GANs in the dataset, also affected data diversity. We found that 3D models with more organic forms increased the depth of the dataset and thus the volumetric diversity of the designs produced. On the other hand, when the safety factors were analyzed, there was a linear decrease in the standard deviation values. Figure 8 shows the average safety factors at each iteration. The circle diameters on the graph are sized according to the standard deviation values of the safety factors, expressing the data diversity. The first training iteration generated the highest data diversity, while this value was lower in the sixth iteration, as expected. Additionally, the ratios of data generated by GANs within the dataset used in each iteration are depicted on the graph. Figures 7 and 8 show the positive conditionability of the designs produced with GANs. Data diversity improves in the desired direction as the number of iterations increases. When evaluating the initial dataset and the datasets developed through subsequent iterations in terms of geometric diversity, innovation, and quality, it was evident that the initial dataset assured both diversity and robustness. This assurance stemmed from the fact that the initial dataset was created using parametric design techniques. In repeated processes, while the design quality generally improved in the desired direction, there was a slight decrease in diversity. This suggested that repetitive processes can somewhat limit design diversity while maintaining certain quality standards. However, these processes also facilitated the emergence of more innovative and applicable designs for real-world applications. The potential of GAN models to produce designs that were dissimilar to the training dataset demonstrated their contribution to innovative design approaches and their scope for producing more original designs. Another criterion taken into account in determining the performance of the generated 3D models was applicability, which refers to the success of the design in real-world applications. Although the GAN model could generate realistic data, in some cases, it generated incomplete or incorrect geometries. This situation revealed the potential of the GAN model to create designs that did not resemble the training dataset. On the other hand, the applicability of designs that did not fully reflect the dataset characteristics was weak. Some models could not be analyzed in the simulation environment due to fragile sections or discontinuities on the 3D models. The proportions of 3D models that could not be analyzed were 12.8%, 12.3%, 11.8%, 12.9%, 11.5%, and 13.1% for the six iterations, respectively. These statistical results showed that the reorganized data did not have a significant effect on applicability. The mechanical properties of the best-performing workpiece generated in the final iteration were precisely validated using Ansys 2022 r2 software. The results from this assessment closely aligned with those obtained through FEA automation. Figure 9 illustrates the mechanical behavior of the component under loading conditions, with a focus on critical sections.