Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80712
Title: Tool-path optimization using neural networks
Authors: Fok, KY 
Ganganath, N 
Cheng, CT 
Iu, HHC
Tse, CK 
Keywords: Additive manufacturing
Tool-path optimization
3D printing
Neural networks
Issue Date: 2019
Publisher: Institute of Electrical and Electronics Engineers
Source: 2019 IEEE International Symposium on Circuits and Systems (ISCAS), Sapporo, Japan, 2019, p. 1-5 How to cite?
Abstract: Tool-path optimization has been applied in manyindustrial applications, including subtractive manufacturing likesdrilling and additive manufacturing likes 3D printing. Theoptimization process involves finding a time-efficient route fortools to visit all the required sites, which is often computationallyintensive. In practice, heuristics and meta-heuristics are used togenerate sub-optimal results within reasonable durations. Theaim of this work is to use artificial neural networks to yieldbetter tool-paths.
URI: http://hdl.handle.net/10397/80712
DOI: 10.1109/ISCAS.2019.8702473
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The following publication K. Fok, N. Ganganath, C. Cheng, H. H. Iu and C. K. Tse, "Tool-Path Optimization using Neural Networks," 2019 IEEE International Symposium on Circuits and Systems (ISCAS), Sapporo, Japan, 2019, pp. 1-5 is available at https://doi.org/10.1109/ISCAS.2019.8702473
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