Optimizing 3D self-supporting topologies for additive manufacturing

Yun Fei Fu, Kazem Ghabraie, Bernard Rolfe, Yanan Wang, Louis N.S. Chiu, Xiaodong Huang

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

2 Citations (Scopus)

Abstract

Topology optimization can generate highly efficient designs without any prior structural configuration. Additive manufacturing (AM) has become its good partner as it can fabricate the complicated geometries obtained by topology optimization. Designing self-supporting topologies is an effective way to reduce the manufacturing cost caused by the use of support structures. This paper combines a newly developed smooth continuum topology optimization algorithm and Langelaar's AM filter to explore smooth 3D self-supporting topologies. The effectiveness of this combination is validated using a 3D numerical example.

Original languageEnglish
Title of host publicationProceedings of ICCMS 2020 - 12th International Conference on Computer Modeling and Simulation and ICICA 2020 - 9th International Conference on Intelligent Computing and Applications
PublisherAssociation for Computing Machinery (ACM)
Pages220-223
Number of pages4
ISBN (Electronic)9781450377034
DOIs
Publication statusPublished - Jun 2020
EventInternational Conference on Computer Modeling and Simulation 2020 - Brisbane, Australia
Duration: 22 Jun 202024 Jun 2020
Conference number: 12th
https://dl.acm.org/doi/proceedings/10.1145/3408066

Publication series

NameACM International Conference Proceeding Series

Conference

ConferenceInternational Conference on Computer Modeling and Simulation 2020
Abbreviated titleICCMS 2020
CountryAustralia
CityBrisbane
Period22/06/2024/06/20
Internet address

Keywords

  • 3D Optimization Problem
  • Additive Manufacturing
  • Self-Supporting Design
  • Topology Optimization

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