Crashworthiness design of a steel–aluminum hybrid rail using multi-response objective-oriented sequential optimization. (October 2017) - Fang, Jianguang; Qiu, Na; An, Xiuzhe; Xiong, Fenfen; Sun, Guangyong; Li, Qing
Highlights: Steel–Aluminum Hybrid structures were optimized to achieve better crashworthiness. Artificial bee colony (ABC) algorithm was adopted to seek the optimum. A multi-response objective-oriented sequential sampling strategy was compared with one-stage sampling strategy. Abstract: Hybrid structures with different materials have aroused increasing interest for their lightweight potential and excellent performances. This study explored the optimization design of steel–aluminum hybrid structures for the highly nonlinear impact scenario. A metamodel based multi-response objective-oriented sequential optimization was adopted, where Kriging models were updated with sequential training points. It was indicated that the sequential sampling strategy was able to obtain a much higher local accuracy in the neighborhood of the optimum and thus to yield a better optimum, although it did lead to a worse global accuracy over the entire design space. Furthermore, it was observed that the steel–aluminum hybrid structure was capable of decreasing the peak force and simultaneously enhancing the energy absorption, compared to the conventional mono-material structure.