## 5 Results and Discussion  
GAN is an effective generative design tool for generating new data similar to the geometrical configurations of training data. The main shortcoming of GAN models in engineering design is that mechanical properties such as elastic modulus, yield strength, and energy absorption cannot be controlled in the generated data. Since the properties to be learned by the GAN model are not specified by the user, it is very difficult to learn these mechanical properties with existing GAN models. Instead, this study proposes an indirect approach to learning mechanical properties. Since GAN models learn from the dataset, building the dataset from only high-structural-performance workpieces ensures that the 3D models generated by the GAN model also exhibit high structural performance. Thus, mechanical features are included in the generative deep learning training process. The proposed design and evaluation cycle aims to improve the mechanical properties of 3D models generated by GAN. In the first stage, a synthetic dataset is created using parametric design techniques to overcome the data shortage problem. The 3 DGAN model is trained with this synthetic dataset in the second stage. In the last stage, 3D models generated by the 3 DGAN model are analyzed in a simulation software to determine the mechanical properties. The 3D models with superior mechanical properties determined here are included in the dataset to be used in the next iteration. Random data are removed from the dataset equal to the amount of new data added. The proposed method analyzes only the effect of data quality on GAN performance without changing the amount of data and training parameters. Strategically, we preferred to indirectly tune the training dataset rather than use a physics-informed GAN. This approach was primarily applied with the aim of exploring the potential of GANs to generate new designs that deviate significantly from standard models while maintaining performance. By editing the training dataset, an attempt was made to provide the GAN model with a variety of examples that enabled it to implicitly learn the desired performance characteristics without explicitly defining them. This method allows the GAN to learn and generate new designs that are not just variations of existing models but also potentially innovative solutions that may not be immediately noticeable with direct coding. There are three reasons why the proposed method is not applied to the initial training dataset. Firstly, pre-filtering the limited training data may leave insufficient data for deep learning model training. Secondly, the reduction in data diversity may cause an over-learning problem. Data diversity is necessary for GANs to learn the common features of a design class. Finally, analyzing the whole dataset in a simulation environment is computationally inefficient. The main objective of this study was not to maximize the performance of a structural component but to demonstrate the contribution of the presented method in improving mechanical properties. Although only one joint model was considered in this study, the proposed method can be applied to various structural components where mechanical properties are crucial, such as brackets, hinges, and load-bearing parts. Similarly, von Mises stress, which provides accurate predictions in multiaxial stress situations, was considered as the primary performance metric for evaluating joint models. However, many other critical material properties, such as elastic modulus, stiffness, fatigue strength, yield strength, and natural frequency, are worthy of consideration in engineering design and evaluation. Integration of other performance metrics into the proposed framework is necessary for a comprehensive evaluation. Figure 7 shows the calculated safety factor values according to the von Mises stress obtained at each iteration step. In the graphs, the x-axis shows the volumes of the generated designs, while the y-axis represents the safety factor of the design.