Research on Denture Modeling Method Based on NURBS Curve Fitting Algorithm

To address the issue of coarse surface fitting after acquiring oral data points with an intraoral scanner, this paper applies NURBS surface fitting technology in the field of dental restoration and analyzes the complete fitting process. For denture surface design, a corresponding control mesh was co...

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Bibliographic Details
Published in2024 3rd International Conference on Artificial Intelligence and Computer Information Technology (AICIT) pp. 1 - 4
Main Authors Fan, Long, Xiong, Tao, Li, Jianhua, Bu, Ningbin
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.09.2024
Subjects
CNC
Fitting
hyperDENT
Mathematical models
NURBS surface fitting
Rough surfaces
Splines (mathematics)
Surface fitting
Surface reconstruction
Surface roughness
Surface topography
Surface treatment
Trajectory
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Summary:To address the issue of coarse surface fitting after acquiring oral data points with an intraoral scanner, this paper applies NURBS surface fitting technology in the field of dental restoration and analyzes the complete fitting process. For denture surface design, a corresponding control mesh was constructed, ultimately resulting in a NURBS surface model of the denture. Compared to traditional surface fitting methods, the resulting surface is more accurate and smoother, optimizing the modeling precision of the denture surface. After obtaining the optimized denture surface, the surface is imported into hyperDENT for toolpath planning, which outputs an NC file to control the CNC system's operation. Then, based on the parametric method, the tool position points are subjected to NURBS curve interpolation fitting using a NURBS curve trajectory algorithm, thereby achieving NURBS-based toolpath generation. Simulation machining data shows that the average length of each NURBS spline curve segment is six times longer than the toolpaths generated by the small segment method, and the number of trajectory segments is only 28% of the original trajectory segments.
DOI:10.1109/AICIT62434.2024.10730043