Design of lightweight tree-shaped internal support structures for 3D printed shell models. Issue 9 (14th October 2019) - Zhu, Lin; Feng, Ruiliang; Li, Xianda; Xi, Juntong; Wei, Xiangzhi
Abstract : Purpose: The purpose of this paper is to design a lightweight tree-shaped internal support structure for fused deposition modeling (FDM) three-dimensional (3D) printed shell models. Design/methodology/approach: A hybrid of an improved particle swarm optimization (PSO) and greedy strategy is proposed to address the topology optimization of the tree-shaped support structures, where the improved PSO is different from traditional PSO by integrating the best component of different particles into the global best particle. In addition, different from FEM-based methods, the growing of tree branches is based on a large set of FDM 3D printing experiments. Findings: The proposed improved PSO and its combination with a greedy strategy is effective in reducing the volume of the tree-shaped support structures. Through comparison experiments, it is shown that the results of the proposed method outperform the results of recent works. Research limitations/implications: The proposed approach requires the derivation of the function of the yield length of a branch in terms of a set of critical parameters (printing speed, layer thickness, materials, etc.), which is to be used in growing the tree branches. This process requires a large number of printing experiments. To speed up this process, the users can print a dozen of branches on a single build platform. Thereafter, the users can always use the function for the fabrication of the 3D models. Originality/value: The proposed approach is useful for the designers and manufacturers to save materials and printing time in fabricating the shell models using the FDM technique; although the target is to minimize the volume of internal support structures, it is also applicable to the exterior support structures, and it can be adapted to the design of the tree-shaped support structures for other AM techniques such as SLA and SLM.