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Academic Journal of Engineering and Technology Science, 2019, 2(3); doi: 10.25236/AJETS.020057.

A slicing parameter optimization method using group search optimization algorithm in STL model for 3D printing application

Author(s)

Zeqing Li 1, 2, *, Xuezhang Zhao 3

Corresponding Author:
Zeqing Li
Affiliation(s)

1. Shenzhen Polytechnic, Shenzhen, Guangdong, 518055, China
2. Tianjin University, Tianjin, 300072, China)
3. Foshan Polytechnic, Foshan, Guangdong, 528137, China
*Corresponding Author: [email protected]

Abstract

In order to improve the accuracy and efficiency of 3D printing, a slicing direction and thickness optimization method based on group search optimization (GSO) algorithm is proposed. Firstly, according to the geometric characteristics of STL data model, the relationship between volume error, production time and layered direction and thickness in 3D printing is analyzed, and a weighted objective function is constructed. Then, the GSO algorithm is used to optimize the solution space to obtain the optimal slicing parameters. The experimental results show that this method can effectively reduce the volume error and improve the printing efficiency.

Keywords

3D printing; slicing parameter optimization; volume deviation; group search optimization algorithm

Cite This Paper

Zeqing Li, Xuezhang Zhao. A slicing parameter optimization method using group search optimization algorithm in STL model for 3D printing application. Academic Journal of Engineering and Technology Science (2019) Vol. 2 Issue 3: 73-84. https://doi.org/10.25236/AJETS.020057.

References

[1] Chizari K, Daoud M A, Ravindran A R, et al(2016). 3D Printing of Highly Conductive Nanocomposites for the Functional Optimization of Liquid Sensors. Small, Vol. 12, no.44, pp. 6176-6176.
[2] Yuan J P, Chen G X(2015). Speedup Method for Paper-Based 3D Color Printing Based on STL File. Applied Mechanics & Materials, Vol. 731, p. 4.
[3] Cai T, Rybicki F J, Giannopoulos A A, et al (2015). The residual STL volume as a metric to evaluate accuracy and reproducibility of anatomic models for 3D printing: application in the validation of 3D-printable models of maxillofacial bone from reduced radiation dose CT images. 3d Printing in Medicine, Vol. 1, no. 1, pp. 1-9.
[4] Rictor A, Riley B(2016). Optimization of a Heated Platform Based on Statistical Annealing of Critical Design Parameters in a 3D Printing Application.. Procedia Computer Science, Vol. 83, pp. 712-716.
[5] Lara-Prieto V, Bravo-Quirino E, Rivera-Campa M Á(2015). An Innovative Self-learning Approach to 3D Printing Using Multimedia and Augmented Reality on Mobile Devices . Procedia Computer Science, Vol. 75, no.1, pp. 59-65.
[6] Vaezi M, Chua C K(2011). Effects of layer thickness and binder saturation level parameters on 3D printing process. International Journal of Advanced Manufacturing Technology, Vol. 53, no.1-4, pp.275-284.
[7] Farzadi A, Solati-Hashjin M, Asadi-Eydivand M, et al(2014). Effect of layer thickness and printing orientation on mechanical properties and dimensional accuracy of 3D printed porous samples for bone tissue engineering. Plos One, Vol. 9, no.9, p.108252.
[8] Li Q, Xu X Y(2016). Self-adaptive slicing algorithm for 3D printing of FGM components. Materials Research Innovations, 2016, 19, no.5, pp. 635-641.
[9] Hsieh C T, Lai E, Shen C L, et al(2015). Saliency-Preserving Slicing Optimization for Effective 3D Printing. Computer Graphics Forum, Vol. 34, no. 6, pp. 148-160.
[10] Optimizing of forming direction and slicing thickness in 3D printing  . Journal of Plasticity Engineering, Vol. 22, no.6):7-10
[11] Song Y, Yang Z, Yuan L, et al(2018). Function representation based slicer for 3D printing. Computer Aided Geometric Design, 2018, Vol. 62, p. S0167839618300268.
[12] Xu H, Jing W, Li M, et al. A slicing model algorithm based on STL model for additive manufacturing processes. Advanced Information Management, Communicates, Electronic & Automation Control Conference, pp. 132-136.
[13] Quan W, Yang P, Ling H, et al(2016). An Adaptive Slicing Thickness Adjustment Method Based on Cloud Point in 3D Printing. International Conference on Embedded Software & Systems, pp. 523-527.
[14] Alipour M, Teimourzadeh S, Seyedi H(2015). Improved group search optimization algorithm for coordination of directional overcurrent relays. Swarm & Evolutionary Computation, no. 23, pp. 40-49.
[15] Tang R, Fong S, Dey N, et al(2017). Cross Entropy Method Based Hybridization of Dynamic Group Optimization Algorithm. Entropy, vol.19, no.10, pp. 533-539.