Distributed optimization architectures decompose large monolithic optimization problems into sets of smaller and more
manageable optimization subproblems. To ensure consistency and convergence towards a global optimum, however, cumbersome coordination is necessary and often not sufficient. A distributed optimization architecture was previously proposed that does not require coordination. This so-called Informed Decomposition is based on two types of optimization
problems: (1) one for system optimization to produce stiffness requirements on components using pre-trained meta models
and (2) one for the optimization of components with two interfaces to produce detailed geometries that satisfy the stiffness
requirements. Each component optimization problem can be carried out independently and in parallel. This paper extends
the approach to three-dimensional structures consisting of components with six degrees of freedom per interface, thus
significantly increasing the applicability to practical problems. For this, an 8-dimensional representation of the general
12 x 12 interface stiffness matrix for components is derived. Meta models for mass estimation and physical feasibility of
stiffness targets are trained using an active-learning strategy. A simple two-component structure and a large robot structure
consisting of four components subject to constraints for 100 different loading scenarios are optimized. The example results
are at most 12.9% heavier than those of a monolithic optimization.
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Distributed optimization architectures decompose large monolithic optimization problems into sets of smaller and more
manageable optimization subproblems. To ensure consistency and convergence towards a global optimum, however, cumbersome coordination is necessary and often not sufficient. A distributed optimization architecture was previously proposed that does not require coordination. This so-called Informed Decomposition is based on two types of optimization
problems: (1) one for system...
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