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: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

6 Citations (Scopus)

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) - Proceedings
Publisher	IEEE
ISBN (Print)	978-1-7281-0397-6
DOIs	https://doi.org/10.1109/ISCAS.2019.8702473
Publication status	Published - May 2019
Externally published	Yes
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
Place	Japan
City	Sapporo
Period	26/05/19 → 29/05/19

Research Keywords

3D printing
Additive manufacturing
Neural networks
Tool-path optimization

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ISCAS.2019.8702473

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@inproceedings{05d2de5b846b4370a0b3951079956cea,

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

year = "2019",

month = may,

doi = "10.1109/ISCAS.2019.8702473",

language = "English",

isbn = "978-1-7281-0397-6",

series = "Proceedings - IEEE International Symposium on Circuits and Systems",

publisher = "IEEE",

booktitle = "2019 IEEE International Symposium on Circuits and Systems (ISCAS) - Proceedings",

address = "United States",

note = "2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 ; Conference date: 26-05-2019 Through 29-05-2019",

}

Fok, K-Y, Ganganath, N, Cheng, C-T, Iu, HH-C & Tse, CK 2019, Tool-path optimization using neural networks. in 2019 IEEE International Symposium on Circuits and Systems (ISCAS) - Proceedings., 8702473, Proceedings - IEEE International Symposium on Circuits and Systems, vol. 2019-May, IEEE, 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) - Proceedings. IEEE, 2019. 8702473 (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2019-May).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Tool-path optimization using neural networks

AU - Fok, Kai-Yin

AU - Ganganath, Nuwan

AU - Cheng, Chi-Tsun

AU - Iu, Herbert Ho-Ching

AU - Tse, Chi K.

PY - 2019/5

Y1 - 2019/5

N2 - 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.

AB - 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.

KW - 3D printing

KW - Additive manufacturing

KW - Neural networks

KW - Tool-path optimization

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85066794473&origin=recordpage

U2 - 10.1109/ISCAS.2019.8702473

DO - 10.1109/ISCAS.2019.8702473

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-1-7281-0397-6

T3 - Proceedings - IEEE International Symposium on Circuits and Systems

BT - 2019 IEEE International Symposium on Circuits and Systems (ISCAS) - Proceedings

PB - IEEE

T2 - 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019

Y2 - 26 May 2019 through 29 May 2019

ER -

Tool-path optimization using neural networks

Abstract

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Conference

Research Keywords

UN SDGs

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