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The vehicle structure is a highly complex system as it is subject to different requirements of many engineering disciplines. Multidisciplinary optimization (MDO) is a simulation-based approach for capturing this complexity and achieving the best possible compromise by integrating all relevant CAE-based disciplines. However, to enable operative application of MDO even under consideration of crash, various adjustments to reduce the high numerical resource requirements and to integrate all disciplines in a target way must be carried out. They can be grouped as follows: The use of efficient optimization strategies, the identification of relevant load cases and sensitive variables as well as the reduction of CAE calculation time of costly crash load cases by so-called finite element (FE) submodels. By assembling these components in a clever way, a novel, adaptively controllable MDO process based on metamodels is developed. There are essentially three special features presented within the scope of this paper: First, a module named global sensitivity matrix which helps with targeted planning and implementation of a MDO by structuring the multitude of variables and disciplines. Second, a local, heuristic and thus on all metamodel types computable prediction uncertainty measure that is further used in the definition of the optimization problem. And third, a module called adaptive complexity control which progressively reduces the complexity and dimensionality of the optimization problem. The reduction of resource requirements and the increase in the quality of results are significant, compared to the standard MDO procedure. This statement is confirmed by providing results for a FE full vehicle example in six load cases (five crash load cases and one frequency analysis).

The majority of parts in modern car bodies is manufactured from sheet metal. Rarely these parts are fully stressed due to design space restrictions and complex requirements. The usage of tailor rolled blanks (TRB) enables the reduction of sheet thickness in areas less loaded and thus reduces part weight. Technically most sheet metal parts are potentially suited for the application of TRB. Economic circumstances like the additional flexible rolling process and technology-specific nesting constraints limit the application to a subset of parts. The search for the best candidate parts taking mass and cost into account is currently challenging. This article presents an optimization strategy for the selection of the parts in a vehicle structure that are best suited for the application of TRB. As a first step, a priori preferencing is performed to select parts based on engineering rules. Using a reduced number of candidate parts, high quality metamodels are trained to perform multiobjective optimizations of all possible combinations of remaining parts, revealing the most efficient part selection under consideration of mass and cost.