2008
DOI: 10.1016/j.ijimpeng.2007.09.003
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Multiobjective optimization of multi-cell sections for the crashworthiness design

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Cited by 217 publications
(84 citation statements)
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“…The optimization design can be achieved by using the surrogate models in the optimization algorithm, such as the multi-objective particle swarm optimization (MOPSO) algorithm and desirability approach. Many studies have used RSM with the optimization algorithm to seek an optimal design for the thin-walled tubes under pure axial [19][20][21], bending [22], and oblique loads [23].…”
Section: Introductionmentioning
confidence: 99%
“…The optimization design can be achieved by using the surrogate models in the optimization algorithm, such as the multi-objective particle swarm optimization (MOPSO) algorithm and desirability approach. Many studies have used RSM with the optimization algorithm to seek an optimal design for the thin-walled tubes under pure axial [19][20][21], bending [22], and oblique loads [23].…”
Section: Introductionmentioning
confidence: 99%
“…The alloy shows a ductility of 8.4% and a proof strength of 105 MPa parallel to the extrusion direction. The plastic-kinematic hardening material model parameters of 6061T4 Al alloy were taken from reference [9] and are tabulated in Table 2. The compression stress-strain curves of Alulight foams at various relative densities are shown in Figure 5(a).…”
Section: Resultsmentioning
confidence: 99%
“…Therefore, an optimisation schedule is often needed in order to reveal the advantages of foam filling. The optimisation of the specific energy absorption (SEA) of the multi-corner thin-walled columns was previously investigated using the response surface methodology (RSM) with several different objective functions including linear, quadratic, cubic and quintic polynomials [9,11]. A minimum error was found, when a quadratic polynomial objective function was used.…”
Section: Introductionmentioning
confidence: 99%
“…To account for the lightweight design and crashworthiness, two objective functions are used: f EA = EA(t) and f m = m(t). The optimization problem is then formulated using the geometrical average method [13]. In this method, the efficiency coefficient of these two objectives is expressed in the form of cost function F E : where it is required to maximize,…”
Section: Optimization Equationsmentioning
confidence: 99%