• Title of article

    Parametric analysis and multiobjective optimization for functionally graded foam-filled thin-wall tube under lateral impact

  • Author/Authors

    Fang، نويسنده , , Jianguang and Gao، نويسنده , , Yunkai and Sun، نويسنده , , Guangyong and Zhang، نويسنده , , Yuting and Li، نويسنده , , Qing، نويسنده ,

  • Issue Information
    روزنامه با شماره پیاپی سال 2014
  • Pages
    11
  • From page
    265
  • To page
    275
  • Abstract
    Foam-filled thin-walled tubes have proven an ideal energy absorber in automotive industry for its extraordinary energy-absorbing ability and lightweight potential. Unlike existing uniform foam (UF), this paper introduces functionally graded foam (FGF) to fill into the thin-walled structure subjected to lateral impact loading, where different configurations of foam grading (axial FGF and two transverse FGFs) are considered. To systematically investigate the bending behavior of this novel structure, numerical model is established using nonlinear finite element analysis code LS-DYNA and then is validated against the experiment. Through parametric study, it is found that the FGF tube absorbs more energy but may produce larger force than the UF counterpart. In addition, various parameters have a considerable effect on the crashworthiness performance of the FGF filled tube. Finally, multiobjective optimizations of UF and FGF filled columns are conducted, aiming to improve the specific energy absorption (SEA) and reduce the maximum impact force simultaneously, based upon the multiobjective particle optimization (MOPSO) algorithm and Kriging modeling technique. The optimization results show that all the FGF filled tubes can produce better Pareto solutions than the ordinary UF counterpart. Furthermore, the axial FGF tube provides better energy absorption characteristics than the two types of transverse FGF tubes.
  • Keywords
    Functionally graded foam (FGF) , Multiobjective Optimization , Kriging model , Energy absorption , Crashworthiness , Three-point bending
  • Journal title
    Computational Materials Science
  • Serial Year
    2014
  • Journal title
    Computational Materials Science
  • Record number

    1692876