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

Fok, K, Cheng, C, Ganganath, N, Iu, H and Tse, C 2018, 'Accelerating 3D Printing Process Using an Extended Ant Colony Optimization Algorithm', in 2018 IEEE International Symposium on Circuits and Systems (ISCAS), Florence, Italy, 27-30 May 2018, pp. 1-5.


Document type: Conference Paper
Collection: Conference Papers

Title Accelerating 3D Printing Process Using an Extended Ant Colony Optimization Algorithm
Author(s) Fok, K
Cheng, C
Ganganath, N
Iu, H
Tse, C
Year 2018
Conference name 2018 IEEE International Symposium on Circuits and Systems (ISCAS)
Conference location Florence, Italy
Conference dates 27-30 May 2018
Proceedings title 2018 IEEE International Symposium on Circuits and Systems (ISCAS)
Publisher IEEE
Place of publication United States
Start page 1
End page 5
Total pages 5
Abstract 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.
Subjects Neural, Evolutionary and Fuzzy Computation
Manufacturing Processes and Technologies (excl. Textiles)
Keyword(s) Ant colony optimization
Additive manufacturing
3D printing
Undirected rural postman problem
DOI - identifier 10.1109/ISCAS.2018.8351113
Copyright notice © 2018 IEEE
ISBN 9781538648810
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