A methodology to investigate and optimise the crashworthiness response of foam-filled twelve right angles thin-walled structures under axial impact

Liang, Rui; Liu, Xi; Hu, Yuling; Jiang, Chengyue; Bastien, Christophe

doi:10.1016/j.compstruct.2023.116736

Cited by 14 publications

(1 citation statement)

References 59 publications

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“…The static and dynamic friction coefficients between the inner, the outer square tube and the clamper were 0.4 [30]. Accordting to previous research [31], the Belytschklo-Lin-Tsay thin-shell elements were utilized to simulate the impactor, the tube wall and the clamping device. The element sizes of tube and clamper were set as 2 × 2 mm 2 , while the that of the impactor was asjusted as 10 × 10 mm 2 to balance the accuracy of the simulation and the computational cost.…”

Section: Finite Element Modelingmentioning

confidence: 99%

A machine learning based optimisation method to evaluate the crushing behaviours of square tubes with rectangular-hole-type initiators

Liang

Liu

et al. 2023

International Journal of Crashworthiness

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Rectangular-hole-type crush initiators have attracted much attention due to their exceptional advantages in improving crashworthiness performance. Unfortunately, undesired deformation buckling modes i.e., bifurcation buckling, can occur and challenge optimization processes. This paper aims remedies to this limitation and proposes a machine learning based optimization method that removes these undesired deformation modes to effectively evaluate the effect of initiators' parameters in improving crush response. First, finite element models of square tubes with different numbers and positions of initiators were built and computed. Second, the complex proportional assessment (COPRAS) method was adopted to rank the crashworthiness characteristics. The results demonstrated that square tubes with two sets of four corner holes along the axial direction were the best choice. Finally, the optimization method based on machine learning was employed to evaluate the influence of the initiator's size on the crushing behaviors for minimizing the initial peak force (IPF), maximizing the energy absorption (EA), and crushing force efficiency (CFE). The results illustrated that the IPF of the optimized design Pareto front upper limit is about 21.94% lower, with 0.08% and 36.67% higher EA and CFE, respectively, than those of the best choice observed from COPRAS. This study highlights the ability of the proposed machine learning based optimization method to improve crashworthiness performance.

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Section: Finite Element Modelingmentioning

confidence: 99%