Organic Open-cell Porous Structure Modeling

ACM Symposium on Computational Fabrication 2020

Lihao Tiana    Lin Lu*a    Weikai Chenb    Yang Xiac    Charlie C. L. Wangd    Wenping Wange   


aShandong University   bTencent US   cDalian University of Technology   dThe University of Manchester   eThe University of Hong Kong  


Through the system of this article, from a distribution of particles, we optimize under the condition of ensuring its connectivity,
and obtain the corresponding pore structure and solid part of the model.

 

Abstract


Open-cell porous structures are ubiquitous in nature and have been widely employed in practical applications. Additive manufacturing has enabled the fabrication of shapes with intricate interior structures; however, a computational method for representing and modeling general porous structures in organic shapes is missing in the literature. In this paper, we present a novel method for modeling organic and open-cell porous structures with porosities and pore anisotropies specified by users or stipulated by applications. We represent each pore as a transformed Gaussian kernel whose anisotropy is defined by a tensor field. The porous structure is modeled as a level surface of combined Gaussian kernels. We utilize an anisotropic particle system to distribute the Gaussian kernels concerning the input tensor field. The porous structure is then generated from the particle system by following the anisotropy specified by the input. We employ Morse-Smale complexes to identify the topological structure of the kernels and enforce pore connectivity. The resulting porous structure can be easily controlled using a set of parameters. We demonstrate our method on a set of 3D models whose tensor field is either predesigned or obtained from the mechanical analysis.


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Acknowledgement

We thank all the anonymous reviewers for their valuable comments and constructive suggestions. This work is supported by grants from NSFC (61972232) and State Key Laboratory of Virtual Reality Technology and Systems (VRLAB2019A01).

 

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