# Enhancing 3D Printing with Procedural Generation and STL Formatting Using Python
## 5 Discussion
### Limitations of Previous Studies  
Feature-based design, which is commonly used in traditional CAD tools, presents significant limitations when applied to AM. This is because AM enables the creation of complex geometry that traditional feature-based CAD systems have difficulty managing effectively. More advanced geometry concepts, such as topology optimization, are essential to fully exploiting the potential of AM, but they do not fit well into traditional feature-based frameworks. For these reasons, we can identify the following limitations of a feature-based design for AM. Complex geometries are feature-based design methods that are typically limited to standard geometric features such as holes, bosses, and filets. These methods have difficulty effectively representing and manipulating the complex, organic shapes that AM can produce. Topology optimization, which generates highly irregular and optimized structures, does not comply with the predefined feature sets in traditional CAD tools. Given the design freedom, AM offers unprecedented design freedom, enabling complex lattice structures, internal channels, and other complex features that are difficult to create and modify using feature-based approaches. Traditional feature-based CAD tools often require workarounds or manual adjustments to handle these complexities, which can be time-consuming and error-prone. In terms of performance and efficiency, the computational overhead associated with managing feature histories and dependencies in feature-based CAD systems can become a bottleneck for the complex models typical of AM. This can lead to inefficiencies in the design process, especially when iterative design and optimization are needed. Topological optimization is considered in terms of advanced geometry concepts for AM. Topological optimization is a mathematical approach that optimizes the arrangement of materials in a given design space for a given set of loads, boundary conditions, and constraints in order to maximize performance; however, topological optimization, unconstrained by traditional feature definitions, can generate high-performance structures ideal for AM. Its advantages include material efficiency (reduces material usage while maintaining or improving structural performance), complex structures (generates organic, non-intuitive shapes that are often impossible to achieve using traditional manufacturing methods but are feasible with AM), and performance optimization (can tailor designs to specific performance criteria such as weight reduction, strength, and thermal management). We highlight the following examples of tools: ANSYS Topology Optimization (AN-SYS), Altair OptiStruct (Altair), and Autodesk Generative Design (Autodesk). Lattice and cellular structures are widely used in AM to create lightweight yet strong components. Due to their complexity and repetitive patterns, these structures cannot be easily represented using a feature-based design. Their advantages include lightweight design (significantly reduces weight while maintaining structural integrity), energy absorption (increases impact resistance and energy absorption capacity), and thermal management (improves heat dissipation through increased surface area. Examples include nTopology (nTopology), Materialise 3-matic, and ParaMatters Cogni-CAD. Feature-based design frameworks, while effective for traditional manufacturing methods, do not meet the needs of AM. Advanced geometric concepts such as topology optimization and lattice structures are essential to fully exploiting the capabilities of AM, but are not compatible with feature-based paradigms. While the study presents compelling evidence of the advantages of procedural generation over traditional 3D modeling methods, several limitations could impact the generalizability and robustness of the findings. For example, [PERSON] et al.