Active-Learning Combined with Topology Optimization for Top-Down Design of Multi-Component Systems

L. Krischer, A. Vazhapilli Sureshbabu, M. Zimmermann

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations

Abstract

In top-down design, optimal component requirements are difficult to derive, as the feasible components that satisfy these requirements are yet to be designed and hence unknown. Meta models that provide feasibility and mass estimates for component performance are used for optimal requirement decomposition in an existing approach. This paper (1) extends its applicability adapting it to varying design domains, and (2) increases its efficiency by active-learning. Applying it to the design of a robot arm produces a result that is 1% heavier than the reference obtained by monolithic optimization.

Original languageEnglish
Pages (from-to)1629-1638
Number of pages10
JournalProceedings of the Design Society
Volume2
DOIs
StatePublished - May 2022
Event17th International Design Conference, DESIGN 2022 - Virtual, Online, Croatia
Duration: 23 May 202226 May 2022

Keywords

  • artificial intelligence (AI)
  • data-driven design
  • systems engineering (SE)
  • topological optimisation

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