To Örebro University

oru.seÖrebro University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A Generative Design Optimization Approach for Additive Manufacturing
Örebro University, School of Science and Technology.ORCID iD: 0000-0001-6821-5727
2019 (English)In: Second International Conference on Simulation for Additive Manufacturing / [ed] F. Auricchio, E. Rank, P. Steinmann, S. Kollmannsberger and S. Morganti, Barcelona, Spain: International Centre for Numerical Methods in Engineering (CIMNE) , 2019, p. 130-141Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we present a generative design optimization (GDO) approach for additive manufacturing (AM) by using topology optimization, support vector machines, cellular lattice structures (CLS), design of experiments, morphing and metamodel-based design optimization. By starting from appropriate design domains, a trade-off curve of design concepts is generated by SIMP-based topology optimization (TO). Then, a smooth implicit representation of the TO-solution is established by classifying the discrete density values using soft non-linear support vector machines (SVM). Instead of using the standard soft non-linear SVM of Cortez and Vapnik, we classify the TO solutions by using the 1-norm SVM of Mangasarian. In such manner, the classification is obtained by linear programming instead ofquadratic programming. The implicit SVM-model is further modified by incorporating cellular lattice structures, such as e.g. Gyroid lattice structures, by applying boolean operators. Design of experiments using finite element analysis are then set up by morphing the CLS-modified SVM models for different volume fractions. Finally, metamodel-based design optimization is performed by using optimal ensembles of polynomial regression models, Kriging, radial basis function networks, polynomial chaos expansion and support vector regression. The steps presented above constitute our proposed generative design optimization approach for additive manufacturing and are presented in more detail in the paper.

Place, publisher, year, edition, pages
Barcelona, Spain: International Centre for Numerical Methods in Engineering (CIMNE) , 2019. p. 130-141
Keywords [en]
Topology optimization, Support vector machines, Lattice Structures, Metamodels
National Category
Applied Mechanics
Identifiers
URN: urn:nbn:se:oru:diva-80213ISI: 000563504100012ISBN: 978-84-949194-8-0 (print)OAI: oai:DiVA.org:oru-80213DiVA, id: diva2:1396892
Conference
2nd International Conference on Simulation for Additive Manufacturing (Sim-AM 2019), Pavie, Italy, September 11-13, 2019
Available from: 2020-02-26 Created: 2020-02-26 Last updated: 2020-09-16Bibliographically approved

Open Access in DiVA

No full text in DiVA

Authority records

Strömberg, Niclas

Search in DiVA

By author/editor
Strömberg, Niclas
By organisation
School of Science and Technology
Applied Mechanics

Search outside of DiVA

GoogleGoogle Scholar

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 159 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf