A hybrid of genetic algorithm and particle swarm optimization for reducing material waste in extrusion-basedadditive manufacturing

PurposeThe purpose of this paper is to report the design of a lightweight tree-shaped support structure for fused deposition modeling (FDM) three-dimensional (3D) printed models when the printing path is considered as a constraint. Design/methodology/approachA hybrid of genetic algorithm (GA) and particle swarm optimization (PSO) is proposed to address the topology optimization of the tree-shaped support structures, where GA optimizes the topologies of the trees and PSO optimizes the geometry of a fixed tree-topology. Creatively, this study transforms each tree into an approximate binary tree such that GA can be applied to evolve its topology efficiently. Unlike FEM-based methods, the growth of tree branches is based on a large set of FDM 3D printing experiments. FindingsThe hybrid of GA and PSO is effective in reducing the volume of the tree supports. It is shown that the results of the proposed method lead to up to 46.71% material savings in comparison with the state-of-the-art approaches. Research limitations/implicationsThe proposed approach requires a large number of printing experiments to determine the function of the yield length of a branch in terms of a set of critical parameters. For brevity, one can print a small set of tree branches (e.g. 30) on a single platform and evaluate the function, which can be used all the time after that. The steps of GA for topology optimization and those of PSO for geometry optimization are presented in detail. Originality/valueThe proposed approach is useful for the designers and manufacturers to save materials and printing time in fabricating complex models using the FDM technique. It can be adapted to the design of support structures for other additive manufacturing techniques such as Stereolithography and selective laser melting.

[1]  Xun Xu,et al.  Support Structures for Additive Manufacturing: A Review , 2018, Journal of Manufacturing and Materials Processing.

[2]  Di Liu,et al.  Research and implementation of a non-supporting 3D printing method based on 5-axis dynamic slice algorithm , 2019, Robotics and Computer-Integrated Manufacturing.

[3]  Guangchun Wang,et al.  Fabrication of water‐soluble poly(vinyl alcohol)‐based composites with improved thermal behavior for potential three‐dimensional printing application , 2017 .

[4]  Xiangzhi Wei,et al.  An Improved Two-Level Support Structure for Extrusion-Based Additive Manufacturing , 2021, Robotics Comput. Integr. Manuf..

[5]  Xiangzhi Wei,et al.  A Tree-Shaped Support Structure for Additive Manufacturing Generated by Using a Hybrid of Particle Swarm Optimization and Greedy Algorithm , 2019, J. Comput. Inf. Sci. Eng..

[6]  Kai Tang,et al.  Curved layer based process planning for multi-axis volume printing of freeform parts , 2019, Comput. Aided Des..

[7]  Jun Qian,et al.  3D printing build orientation optimization for flexible support platform , 2019 .

[8]  Bedrich Benes,et al.  Clever Support: Efficient Support Structure Generation for Digital Fabrication , 2014, Comput. Graph. Forum.

[9]  Jianzhong Fu,et al.  Inclined layer printing for fused deposition modeling without assisted supporting structure , 2018, Robotics and Computer-Integrated Manufacturing.

[10]  G. G. Peters,et al.  Quiescent crystallization of poly(lactic acid) studied by optical microscopy and light‐scattering techniques , 2017 .

[11]  Alain Bernard,et al.  Bio-inspired generative design for support structure generation and optimization in Additive Manufacturing (AM) , 2020 .

[12]  A. E. Eiben,et al.  Comparing parameter tuning methods for evolutionary algorithms , 2009, 2009 IEEE Congress on Evolutionary Computation.

[13]  M. Ramezani,et al.  A new photopolymer extrusion 5-axis 3D printer , 2018, Additive Manufacturing.

[14]  Han Lu,et al.  Generation of a tree-like support structure for fused deposition modelling based on the L-system and an octree , 2019, Graph. Model..

[15]  Daniel Cohen-Or,et al.  Approximate pyramidal shape decomposition , 2014, ACM Trans. Graph..

[16]  Rikard Söderberg,et al.  Individualizing Locator Adjustments of Assembly Fixtures Using a Digital Twin , 2019, J. Comput. Inf. Sci. Eng..

[17]  Ming C. Lin,et al.  Example-guided physically based modal sound synthesis , 2013, ACM Trans. Graph..

[18]  Brian N. Turner,et al.  A review of melt extrusion additive manufacturing processes: II. Materials, dimensional accuracy, and surface roughness , 2015 .

[19]  Ligang Liu,et al.  Support-free frame structures , 2017, Comput. Graph..

[20]  Ligang Liu,et al.  Cost-effective printing of 3D objects with skin-frame structures , 2013, ACM Trans. Graph..

[21]  Charlie C. L. Wang,et al.  Perceptual models of preference in 3D printing direction , 2015, ACM Trans. Graph..

[22]  Jean-Philippe Pernot,et al.  Genetic-algorithm based framework for lattice support structure optimization in additive manufacturing , 2019, Comput. Aided Des..

[23]  JiangJingchao,et al.  Support Optimization for Flat Features via Path Planning in Additive Manufacturing , 2019, 3D Printing and Additive Manufacturing.

[24]  Mourad Zaied,et al.  Resource allocation based on hybrid genetic algorithm and particle swarm optimization for D2D multicast communications , 2019, Appl. Soft Comput..

[25]  Xiangzhi Wei,et al.  Toward Support-Free 3D Printing: A Skeletal Approach for Partitioning Models , 2018, IEEE Transactions on Visualization and Computer Graphics.

[26]  Sam Anand,et al.  Octree data structure for support accessibility and removal analysis in additive manufacturing , 2018, Additive Manufacturing.

[27]  Charlie C. L. Wang,et al.  RoboFDM: A robotic system for support-free fabrication using FDM , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[28]  Haeseong Jee,et al.  Slicing algorithms for multi-axis 3-D metal printing of overhangs , 2015 .

[29]  Kunwoo Lee,et al.  Block-based inner support structure generation algorithm for 3D printing using fused deposition modeling , 2017 .

[30]  Jingchao Jiang,et al.  Optimization of process planning for reducing material waste in extrusion based additive manufacturing , 2019 .

[31]  Radovan Kovacevic,et al.  Process planning for 8-axis robotized laser-based direct metal deposition system , 2017 .

[32]  Yang Liu,et al.  Generating support structures for additive manufacturing with continuum topology optimization methods , 2019, Rapid Prototyping Journal.

[33]  Xiaolong Zhang,et al.  Medial axis tree - an internal supporting structure for 3D printing , 2015, Comput. Aided Geom. Des..

[34]  Nobuyuki Umetani,et al.  Branching support structures for 3D printing , 2014, SIGGRAPH '14.

[35]  Lichao Zhang,et al.  Local Barycenter Based Efficient Tree-Support Generation for 3D Printing , 2019, Comput. Aided Des..

[36]  S. Joshi,et al.  Process planning for five-axis support free additive manufacturing , 2020 .

[37]  Richard M. Everson,et al.  A new approach to the design and optimisation of support structures in additive manufacturing , 2013 .

[38]  Omar Ahmed Mohamed,et al.  Optimization of fused deposition modeling process parameters: a review of current research and future prospects , 2015, Advances in Manufacturing.

[39]  Gershon Elber,et al.  Orientation analysis of 3D objects toward minimal support volume in 3D-printing , 2015, Comput. Graph..

[40]  Rohan Vaidya,et al.  Optimum Support Structure Generation for Additive Manufacturing Using Unit Cell Structures and Support Removal Constraint , 2016 .

[41]  Deok-Soo Kim,et al.  Support-free hollowing with spheroids and efficient 3D printing utilizing circular printing motions based on Voronoi diagrams , 2020 .

[42]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[43]  Xiangzhi Wei,et al.  Design of lightweight tree-shaped internal support structures for 3D printed shell models , 2019, Rapid Prototyping Journal.

[44]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[45]  Yi Sun,et al.  An automatic optimization method for minimizing supporting structures in additive manufacturing , 2020 .

[46]  Charlie C. L. Wang,et al.  General Support-Effective Decomposition for Multi-Directional 3-D Printing , 2018, IEEE Transactions on Automation Science and Engineering.

[47]  Aurelian Zapciu,et al.  FDM process parameters influence over the mechanical properties of polymer specimens: A review , 2018, Polymer Testing.

[48]  Che Chung Wang,et al.  Optimizing the rapid prototyping process by integrating the Taguchi method with the Gray relational analysis , 2007 .

[49]  Ray Y. Zhong,et al.  Investigation of printable threshold overhang angle in extrusion-based additive manufacturing for reducing support waste , 2018, Int. J. Comput. Integr. Manuf..

[50]  Charlie C. L. Wang,et al.  Support-Free Hollowing , 2018, IEEE Transactions on Visualization and Computer Graphics.

[51]  Robert J. Strong,et al.  A review of melt extrusion additive manufacturing processes: I. Process design and modeling , 2014 .

[52]  Ratnadeep Paul,et al.  Optimization of layered manufacturing process for reducing form errors with minimal support structures , 2015 .