## 4 Methodology
### Extract Cross-Sectional Sketches and Reconstruct Features
#### 4.2.3 Reconstructing Features Based on Open CASCADE
Figure 12: Comparative analysis of the results obtained by relevant methods using four metrics: (**a**) Chamfer distance, (**b**) residual error, (**c**) P-coverage, and (**d**) patch-to-patch topology error. We imported the triangular mesh models of the cam and sleeve into our CAD software that we developed and performed the task of mesh segmentation, depicted in Figure 13. The segmented triangular meshes are transformed into distinct features within different regions. For instance, the mechanical parts of cam and sleeve, illustrated in Figure 14, are reconstructed with extrusion features. Subsequently, the cross-sectional sketches are subjected to parametric editing, with linear segment lengths and arc radii annotated with their dimensional values. The system automatically adjusts the sketch contour according to the labeled dimensions. By iteratively applying these procedures to the remaining segmented areas, the entire CAD solid models are reconstructed. Customizing the model through sketch parametric editing enables feature deformation, aligning with variant design objectives. The 3D models post-deformation display unique personalized traits, as demonstrated in Figure 9.0

The 3D models, following variant design, is printed to observe the personalized impact. Stereo Lithography Appearance (SLA) is chosen for the 3D-printing process, and the resulting mold is showcased in Figure 9.1 The 3D-printed parts were assessed based on their appearance and outline. The resulting models accurately reflect the design outputs generated by our CAD software, demonstrating high fidelity to the original design specifications. The outlines and external features of the printed parts align with the intended design, showcasing realistic and precise details. This preliminary evaluation indicates that our method effectively translates digital designs into physical models while maintaining the integrity of their appearance and overall geometry. Figure 9.2 Upon conducting shape segmentation on these triangular meshes, the resulting effects are observed. ## 6 Conclusions

Our study presents a novel computational paradigm for reconstructing solid CAD features from segmented manifold triangular meshes. This approach makes significant contributions by introducing a unified mathematical representation that categorizes common solid CAD features as generalized swept bodies, thus facilitating efficient feature reconstruction. Through the utilization of a numerical optimization-based technique, geometric processing on the segmented meshes allows for the extraction of crucial cross-sectional sketch curves and feature paths. Demonstrating the utilization of this computational paradigm in parametric and variant designs of manifold triangular meshes, we improve their adaptability with personalized 3D-printed models, providing potential pathways for future research in the field. In future research, there may be a shift towards consistent feature reconstruction for polygon meshes of mechanical parts, particularly emphasizing the integration of solid geometry construction (CSG) into the reconstruction process. In addition, a comprehensive accuracy analysis that considers various 3D-printing processes and parameters could be pursued in future research. Saddle points could indeed be a better choice for initial sampling; however, extracting saddle points from the surface of a discrete manifold mesh presents an interesting and challenging task. Additionally, there is a potential for investigating the high-level design semantics present in unstructured polygon meshes. The advancement of generative artificial intelligence technology provides a reliable means of

Figure 9.3 In our developed CAD software, the process for reconstructing features advances from left to right: extracting the cross-sectional sketches, creating the cross-sectional sketch curves, reconstructing the extrusion features, and performing parametric editing to produce the variant design outcomes. Figure 9.4 Personalized 3D-printed models of mechanical parts.