Meshless Voxel Generation Method of Rational Surfaces Towards Stereolithography

With the continuous improvement of laser stereolithography resolution, it is difficult to meet the accuracy requirements of micro-, nano-, and even atomic scales for the existing methods, which commonly generate voxel of 3D manifold by meshing. Therefore, a meshless voxel generation (MVG) method of rational surfaces towards stereolithography is proposed to directly transform solids, represented by non-uniform rational B-splines (NURBS) surfaces, to voxel models of arbitrary precision. Initially, a continuous tunnel-free adaptive sampling method targeted at NURBS surfaces is introduced. The trimming interior sample points are obtained by employing a boundary marking filling algorithm. Subsequently, the sampled points within the parameter domain are mapped to the voxel space, and a surface voxel model is produced based on continuous tunnel-free sampling. The internal voxel fill is thereby carried out using breadth-first search (BFS). Experimental validation, utilizing a micro-structured spatial surface thin-walled propeller blade, testifies the effectiveness of the MVG method in terms of enhancing voxelization precision. Comparative experiments with traditional discrete subdivision sampling methods demonstrate the efficiency of MVG method in improving voxelization precision of rational surface solids. At high voxelization resolutions, through the MVG method, the mean volume error during the voxel discretization process of employed rational solid, which is stitched with multiple continuous surfaces, can be reduced by up to 10.17%.

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Publication Details

Journal
Journal of Computer-Aided Design & Computer Graphics
Published
2024-09-07
DOI
https://doi.org/10.3724/sp.j.1089.2024-00070
Primary Topic
Additive Manufacturing and 3D Printing Technologies
Type
article
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article

Meshless Voxel Generation Method of Rational Surfaces Towards Stereolithography

Shuyou Zhang, Jinghua Xu, Linxuan WANG, Jianrong Tan et al.
Journal of Computer-Aided Design & Computer Graphics
Additive Manufacturing and 3D Printing Technologies
article

Meshless Voxel Generation Method of Rational Surfaces Towards Stereolithography

Shuyou Zhang, Jinghua Xu, Linxuan WANG, Jianrong Tan, Qianyong Chen
article en

Abstract

With the continuous improvement of laser stereolithography resolution, it is difficult to meet the accuracy requirements of micro-, nano-, and even atomic scales for the existing methods, which commonly generate voxel of 3D manifold by meshing. Therefore, a meshless voxel generation (MVG) method of rational surfaces towards stereolithography is proposed to directly transform solids, represented by non-uniform rational B-splines (NURBS) surfaces, to voxel models of arbitrary precision. Initially, a continuous tunnel-free adaptive sampling method targeted at NURBS surfaces is introduced. The trimming interior sample points are obtained by employing a boundary marking filling algorithm. Subsequently, the sampled points within the parameter domain are mapped to the voxel space, and a surface voxel model is produced based on continuous tunnel-free sampling. The internal voxel fill is thereby carried out using breadth-first search (BFS). Experimental validation, utilizing a micro-structured spatial surface thin-walled propeller blade, testifies the effectiveness of the MVG method in terms of enhancing voxelization precision. Comparative experiments with traditional discrete subdivision sampling methods demonstrate the efficiency of MVG method in improving voxelization precision of rational surface solids. At high voxelization resolutions, through the MVG method, the mean volume error during the voxel discretization process of employed rational solid, which is stitched with multiple continuous surfaces, can be reduced by up to 10.17%.

Journal of Computer-Aided Design & Computer GraphicsVol. 37(10)
Openalex Percentile: Top 58%
Additive Manufacturing and 3D Printing Technologies
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Meshless Voxel Generation Method of Rational Surfaces Towards Stereolithography — Shuyou Zhang, Jinghua Xu, et al. · Journal of Computer-Aided Design & Computer Graphics (2024) | TGRS Research Map | TGRS