A comparative study on crashworthiness of thin-walled tubes with functionally graded thickness under oblique impact loadings

The main objective of this study is to investigate the effects of functionally graded thickness (FGT) patterns and cross-sectional shapes (i.e. circular, square and hexagonal) on crashworthiness performance of thin-walled tubes under multiple impact loading angles (0°–30°) by using the nonlinear explicit finite-element (FE) method. In order to show the efficiency of FGT tubes under different impact loading angles, the crashworthiness performances of the FGT tubes are also compared with their uniform thickness (UT) counterparts. At this point, the FGT and UT tubes are designed to have the same height, average cross-section area and weight. In addition, a multigene genetic programming (MGP)-based procedure is first time presented in literature for crashworthiness prediction of thin-walled structures under different impact loadings. To ensure the accuracy of the numerical models, the FE models are validated against both theoretical and experimental results in literature. The results demonstrated that the cross-sectional shapes, gradient exponents and impact loading angles effect the crashworthiness performances of thin-walled tubes, significantly. The simulation results showed that the FGT tubes have a superior crashworthiness performance compared to their UT counterparts especially at high impact loading angles due to the fact that FGT makes possible more folds to be formed and significantly increases the global buckling resistance of tubes. In particular, the SEA values of FGT tubes can reach 93% higher values than that of UT counterparts. The results also showed that the FGT tubes with square cross-section have generally lower energy absorption performance compared with circular and hexagonal ones. Especially, the square FGT tubes have up to 31% lower the SEA values than hexagonal and circular tubes. It is also revealed that the proposed MGP approach is able to predict the crashworthiness parameters with high accuracy.

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    • © 2019 Informa UK Limited, trading as Taylor & Francis Group. Abstract republished with permission of Taylor & Francis.
  • Authors:
    • Baykasoğlu, Cengiz
    • Baykasoğlu, Adil
    • Çetin, Merve Tunay
  • Publication Date: 2019-7


  • English

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  • Accession Number: 01707000
  • Record Type: Publication
  • Files: TRIS
  • Created Date: May 21 2019 3:02PM