Multiobjective optimization for crash safety design of vehicles using stepwise regression model |
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Authors: | Xingtao Liao Qing Li Xujing Yang Weigang Zhang Wei Li |
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Affiliation: | (1) State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Changsha, 410082, China;(2) School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Room S509, Building J07, Sydney, NSW, 2006, Australia |
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Abstract: | In automotive industry, structural optimization for crashworthiness criteria is of special importance. Due to the high nonlinearities,
however, there exists substantial difficulty to obtain accurate continuum or discrete sensitivities. For this reason, metamodel
or surrogate model methods have been extensively employed in vehicle design with industry interest. This paper presents a
multiobjective optimization procedure for the vehicle design, where the weight, acceleration characteristics and toe-board
intrusion are considered as the design objectives. The response surface method with linear and quadratic basis functions is
employed to formulate these objectives, in which optimal Latin hypercube sampling and stepwise regression techniques are implemented.
In this study, a nondominated sorting genetic algorithm is employed to search for Pareto solution to a full-scale vehicle
design problem that undergoes both the full frontal and 40% offset-frontal crashes. The results demonstrate the capability
and potential of this procedure in solving the crashworthiness design of vehicles. |
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Keywords: | Crashworthiness Multiobjective optimization Stepwise regression Finite element method Genetic algorithm |
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