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Indexed by:期刊论文

Date of Publication:2016-11-01

Journal:INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES

Included Journals:SCIE、EI、Scopus

Volume:118

Page Number:21-35

ISSN No.:0020-7403

Key Words:Double-hat beam; Hybrid material; Lateral impact; Multiobjective optimization; Energy absorption

Abstract:An innovative double-hat thin-walled beam comprised of aluminum-steel hybrid materials is proposed for potential application in passenger vehicle bumper systems to reduce pedestrian injury. The beams are featuring an aluminum alloy upper hat and a high strength steel lower hat riveted together to increase the specific energy absorption (SEA) and to reduce the initial peak force (F-ip) simultaneously under lateral impacts. Quasi-static three-point bending test was performed to explore the bending resistant characteristics of such a double-hat beam. Furthermore, bending behavior of the hybrid beam under lateral impact was numerically investigated using LS-DYNA and compared with that of its counterparts with homogeneous materials and identical geometrical dimensions. It was found that the aluminum-steel hybrid beam shows a well-balanced and better bending performance under lateral impact compared to beams made of a single material. Parametric studies were further conducted to investigate the influences of critical geometric parameters on the crashworthiness performances of such double-hat beams under lateral impacts. Based on radial basis function (RBF) metamodels and using non-dominated sorting genetic algorithm (NSGA-II), bi-objective optimizations to maximize SEA and minimize F-ip were carried out for both the hybrid and the two homogeneous beams. The optimization results show that the hybrid beam possesses better Pareto solutions than the homogeneous beams, and is more preferable for use in vehicle bumpers. Finally, a tri-objective optimization problem is solved for the hybrid beam to further maximize its bending moment (Mb). It was found that SEA and Mb of the hybrid beam are cooperating with each other and emphasize on different objectives will result in optimal configurations with slight differences. (C) 2016 Elsevier Ltd. All rights reserved.