## 3 Materials and Methods
### Creating a Dataset and Performing Preparation  
Deep learning methods are AI algorithms that learn patterns from the training set and use them for image processing, object recognition, and generating new data. Therefore, creating and compiling training sets is the main factor in deep learning model success. Figure 1: Flowchart depicting the process of creating functional design options. However, 3D model sets are limited for reasons such as differences in model representation methods [35]. Three basic representation methods are used in 3D object recognition and model generation processes: voxel, point cloud, and polygon mesh. Volumetric pixels used to represent a point in 3D space are called voxels. Point cloud is a data representation method formed by the points on the surface of a 3D structure. Each point is represented by coordinates. Point cloud model representations are often preferred in deep learning model training due to their easy modeling and low computational cost [36; 37]. Finally, polygon mesh is a collection of vertices, edges, and surfaces that define the surfaces of 3D models. It is widely used in applications requiring a realistic appearance and detailed representation [38]. These representation methods have various advantages and limitations. For example, point clouds offer low computational cost and realistic representations but do not provide complete surface description information. Polygon meshes provide detailed surface information but may be less computationally efficient. On the other hand, while voxel-based representations perform well in internal structure descriptions, they are not compatible with applications where high surface quality is required. Therefore, the representation method to be used is determined according to the needs of the process to be applied [36]. In addition, different data representation methods can be used at various stages of an application or project. Another limitation of 3D model recognition and generating studies is the difficulty of accessing accurate, diverse, and high-quality data. Although popular 3D data repositories such as Princeton shape benchmark, McGill 3D shape benchmark, 7 scenes, ModelNet, and ShapeNet contain 3D models of various classes and sizes, they are quite insufficient in terms of having structural component (bracket, hinge, joint, arm, valve, etc.) models [39]. The problem of data deficiency, which reduces the effectiveness of generative design models in engineering design, can be overcome with synthetic datasets created with various algorithms and software. For this purpose, a synthetic dataset was created with an algorithm combining Voronoi diagrams and parametric design. Parametric design is an interdisciplinary field that combines mathematical operations, algorithms, and artificial intelligence with design processes [40; 41]. This approach provides flexible and efficient solutions by automatically and rapidly updating design variants. Although the dataset creation capability of parametric design gives successful results, additional algorithms and techniques are required to increase the quality and diversity of the generated synthetic datasets. One of these techniques, Voronoi diagrams, uses a diagram that divides an area or volume into sections according to specific focal points. These partitions include all points closer to the focal point than any other focal point [42]. Given n focal points in metric space, the Voronoi diagram allows the space \(S=\{s_{1},\,s_{2},\ldots,s_{n}\}\) to be partitioned into n regions. Each Voronoi cell \(V(s_{i})\) consists of all points in space (\(S\)) closer to \(s_{i}\) than any other focal point. The formal expression of a Voronoi cell is shown in Equation (1). Here, \(d(x,y)\) denotes the distance function between points \(x\) and \(y\), while \(S\) denotes the initial set of points.