| Summary: | Significant efforts have been made in the automotive industry to reduce vehicle weight in order to improve vehicle fuel economy and reduce greenhouse gas emissions. New innovations in structural lightweight alloys and manufacturing techniques have allowed automakers to replace conventional steel with lighter aluminum structures. However, automakers have an enormous number of material and gauge thickness combinations to consider in the development process of the next generation production vehicle. Furthermore, the design combination of these materials and structures must not compromise the integrity of the vehicle during a vehicle collision. With the proliferation of inexpensive computational resources, automakers can now explore the effect of material selection on the crashworthiness of next-generation vehicles using computer simulations. While information from these simulations can be manually extracted, the vast amount of data lends itself to artificial intelligence (AI) techniques that can extract knowledge faster and provide more useful interpretations that can be convenient for designers and engineers. This work presents a framework for using artificial intelligence to aid the vehicle design cycle in crashworthiness using aluminum. Virtual experiments of a frontal crash condition of a pick-up truck are performed using finite element analysis to generate the data for this method. Different commercially available aluminum alloys and gauge thicknesses are varied in the virtual experiments. An advanced type of recurrent neural network is used to predict the time-series response of the occupant crash-pulse response, which is a key crashworthiness metric that is used for evaluating safety. This work highlights how automotive designs and engineers can leverage this framework to accelerate the development cycle of the next-generation lightweight vehicle.
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