Research Papers

A Tree-Shaped Support Structure for Additive Manufacturing Generated by Using a Hybrid of Particle Swarm Optimization and Greedy Algorithm

[+] Author and Article Information
Lin Zhu

Institute of Intelligent Manufacturing and Information Engineering,
School of Mechanical Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: zhulin0728@sjtu.edu.cn

Ruiliang Feng

Institute of Intelligent Manufacturing and Information Engineering,
School of Mechanical Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: fengruiliang@sjtu.edu.cn

Xianda Li

Institute of Intelligent Manufacturing and Information Engineering,
School of Mechanical Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: sbnine@sjtu.edu.cn

Juntong Xi

Institute of Intelligent Manufacturing and Information Engineering,
School of Mechanical Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: jtxi@sjtu.edu.cn

Xiangzhi Wei

Institute of Intelligent Manufacturing and Information Engineering,
School of Mechanical Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: antonwei@sjtu.edu.cn

1Corresponding author.

Manuscript received November 11, 2018; final manuscript received March 20, 2019; published online June 7, 2019. Assoc. Editor: Yong Chen.

J. Comput. Inf. Sci. Eng 19(4), 041010 (Jun 07, 2019) (12 pages) Paper No: JCISE-18-1299; doi: 10.1115/1.4043530 History: Received November 11, 2018; Accepted March 26, 2019

Reducing the volume of support structures is a critical means for saving materials and budgets of additive manufacturing, and tree structure is an effective topology for this purpose. Although a few articles in literature and commercial software have been devoted to developing tree-supports, those tree-supports are generated based on geometry optimization or user-defined parameters, which cannot guarantee a minimum volume with robust fabrication guarantee. To address this issue, we propose a set of formulas for stably growing the tree-supports with physical constraints based on 3D printing experiments using fused decomposition modelling (FDM) machines, and a volume minimization mechanism using a hybrid of particle swarm optimization (PSO) method and a greedy algorithm. We show that this combination is effective in reducing the volume of tree-supports and the simulations reveal that the volume curves monotonically descent to a constant within a short time, and our experimental results show that the models with the tree-supports can be manufactured stably.

Copyright © 2019 by ASME
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Grahic Jump Location
Fig. 1

Illustration of some types of support structures used in additive manufacturing: (a) pillar, (b) cone, (c) honeycomb, (d) grid, (e) wall, and (f) tree

Grahic Jump Location
Fig. 2

(a) The parameters that influence the growing length of a bar, the bottom of the bar is fixed on a platform to prevent it from being toppled by the nozzle and (b) a bar printed by an FDM 3D printer, where the coarse bed and the dense bed are automatically generated by the printer to hold the bar

Grahic Jump Location
Fig. 3

Processing the joint of a pair of bars with one above another

Grahic Jump Location
Fig. 4

Processing the leaves and root of a tree

Grahic Jump Location
Fig. 5

3D printing of overhanging bridges, the numbers indicate the horizontal printing length of the bridges without using any support, the zoomed in view of the bridges are shown on top of the figure

Grahic Jump Location
Fig. 6

Discretizing the support space of M for generating a tree-support: (a) the sampling support points on the overhang regions; (b) the grid G; (c) a 2D view of nodes on the highlighted plane in (b), and w is taken in to G for its distance away from node v; (d) the use of node w in helping connect node u to be rooted at point w′ on the surface of M. q is not a grid node in the support space of M and is marked as a box.

Grahic Jump Location
Fig. 7

Illustration of connecting a set of nodes at level j to a node at level j–k, k ∈ [1, j − 1]

Grahic Jump Location
Fig. 8

Illustration of connection topology preservation and mutation by using a greedy algorithm: (a) from the left image to the middle image, the connection topology of the tree is preserved, from the middle image to the right image, the connection topology is mutated with one tree being split into two trees, the red links indicate the new branches generated by the greedy algorithm and (b) a 3D illustration where the dashed circles indicate that the greedy algorithm works by connecting the bottom center of a cone-shaped tip onto the model surface

Grahic Jump Location
Fig. 9

The simulation results for a set of models, from left to right: the initial state, the 100th iteration, the 500th iteration, and the 2000th iteration

Grahic Jump Location
Fig. 10

The volume curve of tree-supports with respect to the number of iterations

Grahic Jump Location
Fig. 11

Illustration of two examples of tree-supports designed with the approach of Ref. [25], the wall thickness of the tree-supports is also highlighted in the figure

Grahic Jump Location
Fig. 12

Effect of the tip diameter: (a) 0.5 mm and (b) 1.6 mm

Grahic Jump Location
Fig. 13

A comparison of the 3D printed models for the Autodesk Meshmixer™ (first and third column) and our approach (second and fourth column)

Grahic Jump Location
Fig. 14

Illustration of the results generated by merely running PSO on grid G



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