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Multiobjective sequential optimization for a vehicle door using hybrid materials tailor-welded structure

Multiobjective sequential optimization for a vehicle door using hybrid materials tailor-welded... To achieve lightweight vehicle door, this paper presents a novel design with a hybrid material tailor-welded structure (HMTWS). A multiobjective optimization procedure is adopted to generate a set of solutions, in which the door stiffness and mass are taken as objective functions, and the material types and plate thicknesses are regarded as the discrete and continuous design variables, respectively. To improve the optimization efficiency, Kriging algorithm is used for generating surrogate model through a sequential sampling strategy. The non-dominated sorting genetic algorithm II (NSGA-II) is employed to perform the multiobjective optimization. It is found that for the same computational cost, the sequential sampling strategy can yield more accurate optimization results than the conventional one-step sampling strategy. Most importantly, HMTWS is found more competent than the traditional thin-walled configurations made of steel or other lighter mono-materials for maximizing the usage of materials and stiffness of the vehicular door structures. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png "Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science" SAGE

Multiobjective sequential optimization for a vehicle door using hybrid materials tailor-welded structure

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References (43)

Publisher
SAGE
Copyright
© IMechE 2015
ISSN
0954-4062
eISSN
2041-2983
DOI
10.1177/0954406215607901
Publisher site
See Article on Publisher Site

Abstract

To achieve lightweight vehicle door, this paper presents a novel design with a hybrid material tailor-welded structure (HMTWS). A multiobjective optimization procedure is adopted to generate a set of solutions, in which the door stiffness and mass are taken as objective functions, and the material types and plate thicknesses are regarded as the discrete and continuous design variables, respectively. To improve the optimization efficiency, Kriging algorithm is used for generating surrogate model through a sequential sampling strategy. The non-dominated sorting genetic algorithm II (NSGA-II) is employed to perform the multiobjective optimization. It is found that for the same computational cost, the sequential sampling strategy can yield more accurate optimization results than the conventional one-step sampling strategy. Most importantly, HMTWS is found more competent than the traditional thin-walled configurations made of steel or other lighter mono-materials for maximizing the usage of materials and stiffness of the vehicular door structures.

Journal

"Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science" – SAGE

Published: Oct 1, 2016

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