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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2013.8
- 수록면
- 103 - 110 (8page)
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초록· 키워드
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Nowadays, manufacturers use computer-aided design for simulations. The reason is that the cost of performance test using actual products is expensive, so the cost of performance simulation using CAD models is lower than it. In this paper, we think about hexahedral meshes for finite element analysis, because simulations with hexahedral mesh are more accurate than those with tetrahedral meshes.
Our purpose is automatic hexahedral mesh generation with sharp features to precisely represent the corresponding features of a target shape. Our hexahedral mesh is generated by a voxel-based algorithm. Thus, in our previous works, we fit the surface of the voxels to the target surface using Laplacian energy minimization. We used normal vector in the fitting for preserving sharp features.
However, the method can not represent concave sharp features precisely. Thus, we improve the previous Laplacian energy by adding a term that depends on multi-normal vectors instead of a term using normal vectors. Furthermore, we accentuate convex/concave subset of surface to represent concave sharp features.
Our purpose is automatic hexahedral mesh generation with sharp features to precisely represent the corresponding features of a target shape. Our hexahedral mesh is generated by a voxel-based algorithm. Thus, in our previous works, we fit the surface of the voxels to the target surface using Laplacian energy minimization. We used normal vector in the fitting for preserving sharp features.
However, the method can not represent concave sharp features precisely. Thus, we improve the previous Laplacian energy by adding a term that depends on multi-normal vectors instead of a term using normal vectors. Furthermore, we accentuate convex/concave subset of surface to represent concave sharp features.
목차
Abstract
1. Introduction
2. Previous Hexahedral Meshing
3. Fitting Algorithm that Represent Concave Sharp Features
4. Result
5. Conclusion
References
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