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

摘要

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.