Tool-path optimization using neural networks

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

doi:10.1109/ISCAS.2019.8702473

Tool-path optimization using neural networks

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

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

5 Citations (Scopus)

271 Downloads (Pure)

Abstract

Tool-path optimization has been applied in many industrial applications, including subtractive manufacturing likes drilling and additive manufacturing likes 3D printing. The optimization process involves finding a time-efficient route for tools to visit all the required sites, which is often computationally intensive. In practice, heuristics and meta-heuristics are used to generate sub-optimal results within reasonable durations. The aim of this work is to use artificial neural networks to yield better tool-paths.

Original language	English
Title of host publication	2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings
Publisher	IEEE, Institute of Electrical and Electronics Engineers
ISBN (Electronic)	9781728103976
DOIs	https://doi.org/10.1109/ISCAS.2019.8702473
Publication status	Published - 1 Jan 2019
Event	2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Sapporo, Japan Duration: 26 May 2019 → 29 May 2019

Publication series

Name	Proceedings - IEEE International Symposium on Circuits and Systems
Volume	2019-May
ISSN (Print)	0271-4310

Conference

Conference	2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019
Country/Territory	Japan
City	Sapporo
Period	26/05/19 → 29/05/19

Access to Document

10.1109/ISCAS.2019.8702473

Fok et al., 2019 Tool-Path Optimization using neural networksAccepted author manuscript, 721 KB

Cite this

Fok, K. Y., Ganganath, N., Cheng, C. T., Iu, H. H. C., & Tse, C. K. (2019). Tool-path optimization using neural networks. In 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings Article 8702473 (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2019-May). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ISCAS.2019.8702473

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title = "Tool-path optimization using neural networks",

abstract = "Tool-path optimization has been applied in many industrial applications, including subtractive manufacturing likes drilling and additive manufacturing likes 3D printing. The optimization process involves finding a time-efficient route for tools to visit all the required sites, which is often computationally intensive. In practice, heuristics and meta-heuristics are used to generate sub-optimal results within reasonable durations. The aim of this work is to use artificial neural networks to yield better tool-paths.",

keywords = "3D printing, Additive manufacturing, Neural networks, Tool-path optimization",

author = "Fok, {Kai Yin} and Nuwan Ganganath and Cheng, {Chi Tsun} and Iu, {Herbert Ho Ching} and Tse, {Chi K.}",

year = "2019",

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doi = "10.1109/ISCAS.2019.8702473",

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publisher = "IEEE, Institute of Electrical and Electronics Engineers",

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Fok, KY, Ganganath, N, Cheng, CT, Iu, HHC & Tse, CK 2019, Tool-path optimization using neural networks. in 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings., 8702473, Proceedings - IEEE International Symposium on Circuits and Systems, vol. 2019-May, IEEE, Institute of Electrical and Electronics Engineers, 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019, Sapporo, Japan, 26/05/19. https://doi.org/10.1109/ISCAS.2019.8702473

Tool-path optimization using neural networks. / Fok, Kai Yin; Ganganath, Nuwan; Cheng, Chi Tsun et al.
2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings. IEEE, Institute of Electrical and Electronics Engineers, 2019. 8702473 (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2019-May).

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

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Tool-path optimization using neural networks

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