Preference-based Topology Optimization of Body-in-white Structures for Crash and Static Loads

Topology optimization methods are increasingly applied tools for the design of lightweight structural concepts in the automotive design process. Ideally, topology optimization provides the optimum distribution of material within a user-defined design space for a given objective function. In the vehicle design process, two important objectives are to maximize stiffness of components for regular working conditions and to maximize energy absorption in exceptional loading conditions, for instance in crash events. For these objective functions, the Hybrid Cellular Automata algorithm devises efficient structures in case of the separated disciplines, by heuristically aiming for a uniform distribution of energy densities. Recently, it was demonstrated that a concurrent optimization of crash and static load cases can be performed by a linear weighting, in which the user preference is separated from the scaling of the internal energies. In this paper, the approach is applied to the practical example of a vehicle body-in-white design, which is optimized for multiple crash and linear static load cases. By comparing resulting internal energies of different load case settings we demonstrate that the hybrid cellular automata algorithm with scaled energy weighting is capable to find a very good trade-off solution within a single concurrent optimization run.