Design for optimal commonality in product families is different from design for optimal performance. While optimal performance may be achieved by the choice of appropriate design parameter values for all system components, optimal commonality requires a particular scheme of sharing components among systems. The number of possibilities to share components can be quantified by Bell's number and becomes large quickly, thus making optimization extremely expensive. This paper presents an approach to identify components that may be shared in order to optimize commonality for a product family of arbitrary high-dimensional nonlinear systems. Solution spaces are computed for each system using iterative Monte Carlo sampling. On these solution spaces, all design goals are reached. They are expressed as sets of permissible intervals for all design parameters. When parameter intervals from different systems overlap, they may assume the same value and components may be shared. The approach is applied to vehicle chassis design. A set of common components is computed for 13 vehicles with ten design parameters each, such that all design goals are satisfied and the number of different component designs is small.

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