Monolithic optimization of large mechanical systems can be expensive and cumbersome. Drivers of computational cost and integration effort are, e.g., the size of the design problem and the number of different components, models, and disciplines. Distributed optimization schemes decompose large problems into smaller subproblems; however, they typically require intense coordination effort. This paper proposes an approach for complete decoupling by decomposing a monolithic optimization into independent optimization subproblems that can be solved without need for coordination. This is accomplished by sampling the space of component performance, here represented by eigenvalues and eigenvectors of stiffness matrices, and establishing meta models that map the relevant component performance values onto feasibility and mass estimates. The optimization procedure consists of two steps: First, a system optimization problem is solved by assigning stiffness requirements to components that are approximately feasible and mass-optimal. Second, the component optimization problems are solved independently of each other such that stiffness requirements are satisfied. As information on feasibility and mass is provided during system optimization by meta models, the approach will be referred to as informed decomposition. The effectiveness of the approach is demonstrated by minimizing the mass of a simple two-component linear structure subject to a requirement on total stiffness. This is done for three different component models, a beam with constant cross-section, a beam with varying cross-sections, and an arbitrary 2-dimensional body, using parametric and topology optimization, respectively. The approach produces results that are at most 1 % heavier than the results obtained by monolithic optimization.
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.
The industry is currently changing rapidly. Both customers and employees are focusing much more on their own needs. On the one hand, this requires individualized products and, on the other hand, development processes need to be aligned not only more efficiently but also more closely to the needs of employees. Agile development combines these two characteristics and the second point can be further improved through analyses for collaboration. This is not only necessary for consumer products, but also in medical technology, more and more individualized solutions are required to better help patients. This is also the case with the examination of cells using micro titer plates, which is the subject of this project. Due to the interaction of research activities both on the process and on the product side, this paper presents research results regarding agile product development and collaboration analysis of physical products on the one hand and research results regarding additive and biocompatible production of microtitration plates on the other hand.
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