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

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 decompos...

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Bibliographic Details
Main Authors: Krischer, L., Vazhapilli Sureshbabu, A., Zimmermann, M.
Format: Conference Proceeding
Language:English
Subjects:
Artificial intelligence
Decomposition
Design optimization
Load
Product development
Robots
Systems design
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Summary: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.
ISSN:2732-527X
2732-527X
DOI:10.1017/pds.2022.165