This paper presents an automated algorithm for the design of vehicle structures for crashworthiness based on the analyses of the structural crash mode (CM), The CM is the history of the deformation of the different zones of the vehicle structure during a crash event. The algorithm emulates a manual design process called crash mode matching where crashworthiness is improved by manually modifying the design until its CM matches what the designers deem as optimal. Given an initial design and a desired crash mode, the proposed algorithm iteratively finds new designs that have better crashworthiness performance via stochastic sampling of the design space. In every iteration of the algorithm, a number of sample designs are generated through a normal distribution on neighboring regions of the search space to the current design, and the best among the samples is chosen as the new design. The mean and the standard deviation of the normal distributions are adjusted in each iteration by examining the crash mode of the current design and by applying a set of fuzzy logic rules that encapsulate elementary knowledge of the CM matching practice. Two case studies, examining a front half vehicle as well as fully detailed vehicle models, are presented to demonstrate the effectiveness of the proposed algorithm.
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