This article introduces a novel constraining approach for structural optimization, which aims to support the conceptual engineer during the early embodiment phase for structural lightweight design. It reduces the time spent on structural engineering studies by enabling optimization algorithms to detect geometric intersections by analyzing the mesh information. This article reviews approaches from the literature focusing on CAD-environments, sampling methods, data analytics and optimization techniques for design and sizing optimization with FE-models. The evaluated approaches are integrated into a Python-based optimization environment. Accordingly, the introduced methodology enables the environment to handle geometric infeasible designs. The presentation of the first results focuses on the feasibility of structural assemblies and the results demonstrate the viability of the NSGA-II for optimization tasks. The example considers the design of a generic b-pillar structure under crashsafety requirements. Using this approach, the NSGA-II algorithm avoids geometric infeasible areas and comparably increases structural performance.

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When designing complete systems or system components, it is of vital importance for the manufacturers to optimally fulfill the continuously increasing demands pertaining to safety, durability, reduction of energy consumption, noise reduction, improvement of comfort, accuracy, etc. This applies to all types of traffic and transportation systems like rail vehicles, automobiles, airplanes and ships. By combining structural analysis and simulation methods with optimization algorithms, required specifications can be met faster and more reliably, and hence the production development cycles can be substantially reduced. This paper shall give an overview on results of a method with the features of a damage approximation as precisely as possible on the one hand and, on the other hand, a load-time history with few different load cycles so that a nonlinear calculation can be performed in the shortest possible time. Simulations with rigidly and elastically modeled components like bogie frames or carbodies show that depending on the type of modeling substantial differences may occur with respect to dynamic behavior and the interaction quantity between the bodies. This aspect has to be taken into consideration for quantitatively sufficient fatigue strength and durability calculation. Mathematical optimization procedures are in general an efficient tool to guarantee the optimal fulfillment of all required design objectives and constraints in all stages of the design process. Some of the procedures are illustrated at two examples (bogie frame, carbody).