Accelerating 3D Printing Process Using an Extended Ant Colony Optimization Algorithm

Kai Yin Fok, Chi Tsun Cheng, Nuwan Ganganath, Herbert Ho Ching Iu, Chi K. Tse

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)


Ant colony optimization (ACO) algorithms have been widely adopted in solving combinatorial problems, like the traveling salesman problem (TSP). Nevertheless, with a proper transformation to TSP, ACO is capable of solving undirected rural postman problems (URPP) as well. In fact, nozzle path planning problems in 3D printing can be represented as URPP. Therefore, in this work, ACO is utilized as a URPP solver to accelerate the printing process in fused deposition modeling applications. Furthermore, mechanisms which exploit unique properties in 3D models are proposed to further extend the ACO in the above optimization process. These mechanisms are capable of accelerating ACO by adaptively adjusting its number of iterations on-the-fly. Simulation results using real-life 3D models show that the proposed extensions can accelerate ACO without affecting the quality of its solutions significantly.

Original languageEnglish
Title of host publication2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538648810
Publication statusPublished - 26 Apr 2018
Event2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Florence, Italy
Duration: 27 May 201830 May 2018

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)0271-4310


Conference2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018


  • 3D printing
  • Additive manufacturing
  • Ant colony optimization
  • Undirected rural postman problem

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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