The Visual Computer

, Volume 33, Issue 6–8, pp 949–960 | Cite as

Cross section-based hollowing and structural enhancement

  • Weiming Wang
  • Baojun Li
  • Sicheng Qian
  • Yong-Jin Liu
  • Charlie C. L. Wang
  • Ligang Liu
  • Baocai Yin
  • Xiuping LiuEmail author
Original Article


Recently, 3D printing has become a powerful tool for personal fabrication. However, the price of some materials is still high which limits its applications in home users. To optimize the volume of the model, while not largely affecting the strength of the objects, researchers propose algorithms to divide the model with different kinds of lightweight structures, such as frame structure, honeycomb cell structure, truss structure, medial axis tree. However, these algorithms are not suitable for the model whose internal space needs to be reused. In addition, the structural strength and static stability of the models, obtained with modern 3D model acquirement methods, are not guaranteed. In consequence, some models are too fragile to print and cannot be survived in daily usage, handling, and transportation or cannot stand in a stable. To handle the mentioned problems, an algorithm system is proposed based on cross sections in this work. The structural weak cross sections are enhanced, and structural strong cross sections are adaptively hollowed to meet a given structural strength, static stability, printability, etc., while the material usage is minimized. The proposed algorithm system has been tested on several typical 3D models. The experimental results demonstrate the effectiveness and practicability of our system.


3D printing Cross section Adaptive hollowing Structural enhancement 



We would like to thank the reviewers for their detailed comments and suggestions which greatly improved the manuscript. The research leading to these results has received funding from China Postdoctoral Science Foundation (2016M601308), the One Hundred Talent Project of the Chinese Academy of Sciences, Fundamental Research Fund (DUT16RC(3)061), National Natural Science Foundation of China (61370143, 61432003, 61661130156, 61672482, 11626253, 11472073 ), Hong Kong RGC GRF (14207414), and Royal Society-Newton Advanced Fellowship (NA150431).


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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Weiming Wang
    • 1
  • Baojun Li
    • 1
  • Sicheng Qian
    • 1
  • Yong-Jin Liu
    • 2
  • Charlie C. L. Wang
    • 3
  • Ligang Liu
    • 4
  • Baocai Yin
    • 1
  • Xiuping Liu
    • 1
    Email author
  1. 1.Dalian University of TechnologyDalianChina
  2. 2.Tsinghua UniversityBeijingChina
  3. 3.Delft University of TechnologyDelftNetherlands
  4. 4.University of Science and Technology of ChinaHefeiChina

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