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 paperConference paper

164 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 languageEnglish
Title of host publication2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISBN (Electronic)9781728103976
DOIs
Publication statusPublished - 1 Jan 2019
Event2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Sapporo, Japan
Duration: 26 May 201929 May 2019

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2019-May
ISSN (Print)0271-4310

Conference

Conference2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019
CountryJapan
CitySapporo
Period26/05/1929/05/19

Fingerprint

Dive into the research topics of 'Tool-path optimization using neural networks'. Together they form a unique fingerprint.

Cite this