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Multi-objective robust optimization of multi-directional carbon/glass fibre-reinforced hybrid composites with manufacture related uncertainties under flexural loading

机译:弯曲载荷下具有制造不确定性的多方向碳/玻璃纤维增​​强混杂复合材料的多目标鲁棒优化

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Multi-objective robust optimization of multi-directional carbon/glass fibre-reinforced epoxy hybrid composites has been presented in this study. Two conflicting objectives, namely minimizing the density and minimizing the cost under the constraint of a specified minimum flexural strength, were considered with the design variables being the fibre type, fibre orientation angle and fibre volume fraction of each lamina. A modified version of the non-dominated sorting genetic algorithm (NSGA-II) was used in order to find the Pareto optimal solutions. Furthermore, robust optimization problems were defined by including manufacturing related uncertainties in the thickness and orientation of each lamina. These uncertain variables were considered as uncertain-but-bounded with the worst case for the minimum flexural strength being determined through an internal antioptimization solver. The optimization and anti-optimization problems were solved with Pareto optimal and robust solutions being presented for carbon/glass fibre hybrid composites with different levels of minimum flexural strength. The results showed that, in general, consideration of uncertainties in thickness and fibre orientation angle increased the material cost and/or density with this effect being more important for high strength composites.
机译:这项研究提出了多方向的碳/玻璃纤维增​​强环氧杂化复合材料的多目标鲁棒优化。考虑了两个相互矛盾的目标,即在指定的最小弯曲强度的约束下使密度最小化和成本最小化,设计变量是每个薄片的纤维类型,纤维取向角和纤维体积分数。为了找到帕累托最优解,使用了非支配排序遗传算法(NSGA-II)的修改版本。此外,通过在每个薄片的厚度和方向上包括制造相关的不确定性来定义鲁棒的优化问题。这些不确定变量被认为是不确定的,但在最坏的情况下会受到限制,因为通过内部反优化求解器确定最小弯曲强度。针对具有不同水平的最小弯曲强度的碳/玻璃纤维混合复合材料,提出了帕累托最优和鲁棒的解决方案,从而解决了优化和反优化问题。结果表明,总的来说,考虑厚度和纤维取向角的不确定性会增加材料成本和/或密度,这种影响对高强度复合材料而言更为重要。

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