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Multi-objective robust optimisation of unidirectional carbon/glass fibre reinforced hybrid composites under flexural loading

机译:弯曲载荷下单向碳/玻璃纤维增​​强混杂复合材料的多目标鲁棒优化

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A multi-objective robust optimisation (MORO) of carbon and glass fibre-reinforced hybrid composites under flexural loading based on an a posteriori approach has been presented in this paper. The hybrid composite comprised of T700S carbon/epoxy laminate at the tensile side and E glass/epoxy laminate at the compressive side. The conflicting objectives for optimisation were to minimise the cost and weight of the composite subject to the constraint of a minimum specified flexural strength. Fibre angles and thicknesses of each lamina were considered as uncertain but bounded variables with the worst-case analyses being performed as a non-probabilistic method and the effect of uncertainties being determined. A hybrid multi-objective optimisation evolutionary algorithm (MOEA) was introduced through modification of an elitist non-dominated sorting genetic algorithm (NSGA-II) and combining it with the fractional factorial design method. The performance of the hybrid algorithm was found to be superior to that of the original version of NSGA-II. The multi-objective robust optimisation of the hybrid composite was solved by utilising the proposed algorithm for several levels of strength with the robust Pareto optimal sets being generated and compared. Three scenarios have been considered to illustrate the applicability of the obtained solutions in an a posteriori decision making process. (C) 2015 Elsevier Ltd. All rights reserved.
机译:本文提出了一种基于后验方法的碳纤维和玻璃纤维增​​强混杂复合材料在弯曲载荷下的多目标鲁棒优化(MORO)。杂化复合材料由拉伸侧的T700S碳/环氧树脂层压板和压缩侧的E玻璃/环氧树脂层压板组成。优化的矛盾目标是在最小指定抗弯强度的约束下使复合材料的成本和重量最小化。每个薄片的纤维角度和厚度都被认为是不确定的但有界的变量,最坏情况下的分析是一种非概率方法,并且确定了不确定性的影响。通过修改精英非支配排序遗传算法(NSGA-II)并将其与分数阶乘设计方法相结合,引入了一种混合多目标优化进化算法(MOEA)。发现混合算法的性能优于原始版本的NSGA-II。通过将提出的算法用于多个级别的强度,并生成并比较了鲁棒的帕累托最优集,从而解决了混合复合材料的多目标鲁棒优化问题。已经考虑了三种方案来说明获得的解决方案在后验决策过程中的适用性。 (C)2015 Elsevier Ltd.保留所有权利。

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