## 5 Results and Discussion
Here, the highest stress occurs at the area farthest from the point of force application and closest to the support point, while other sections experience lower stress levels. The figure also provides information on the von Mises stresses and weights of the original, GAN-generated, and edited models. Initially, the original model has a relatively high weight (579.79 g) but its sharp corners lead to stress concentrations. The GAN-generated model, despite its lower weight (266.39 g), exhibits mechanical properties similar to the original model. This model was further enhanced by manually thickening the thin sections and removing volumes not subjected to stress, achieving better mechanical performance characteristics (43,618 MPa). Figure 7: Volume and safety factors of the generated designs. The proposed design and evaluation framework enables the automation of the following steps carried out in the traditional design process: needs identification, idea and concept generation, modeling, evaluation, and redesign. Thus, product development processes,  
Figure 8: Average safety factors for training iterations. Figure 9: Verification of finite element analysis automation. (**a**) Original model, (**b**) GAN-generated model, (**c**) edited model. which are part of the product life cycle, can be performed automatically without human intervention. This minimizes human errors, shortens the design process, and saves on qualified labor. Furthermore, compared to traditional topology optimization, this method aims to generate a variety of feasible design options rather than a single ideal design option. With the network weights obtained at the end of training, designs with superior mechanical properties can be produced in seconds. The inclusion of finite element analysis in the GAN training process enables the generation of designs with targeted mechanical performance. Moreover, the generative deep learning model, which is partially trained with a limited amount of data, can produce an unlimited number of functional 3D models. This generative design framework offers a potential solution to the problem of data shortage thanks to its capacity to generate new datasets. This feature offers a new way to create the necessary training data for deep learning studies, especially in the defense, aerospace, and automotive industries where the number of data points is insufficient and/or data are difficult to access due to confidentiality. Training 3 DGANs directly with 3D model data makes them suitable for many different applications. On the other hand, this approach presents certain potential challenges and limitations. One notable issue is that as the mechanical performance of the generated designs improves, there is a corresponding increase in the volume of these designs. In addition, it may be necessary to develop more complex GAN architectures because of the need for a high learning capacity in large datasets. This requires more computational power and time. At the same time, some targeted mechanical properties may be more difficult to learn. In addition, if factors such as real-world material properties, manufacturing methods, and conditions of use are ignored, the generated designs may not perform as expected in practical applications. Furthermore, this method cannot take into account traditional manufacturing constraints. This may limit the use and application of designs generated by GANs in real-world applications. For example, while a design may be suitable for 3D printing, it may not be suitable for casting or machining. Therefore, future work may consider incorporating manufacturing-process-specific constraints into the GAN model. While the 3 DGAN model enables the creation of organic and fine-geometry designs, these designs are difficult to manufacture with traditional manufacturing methods due to limitations in tooling and the need for support structures.