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Topology optimization of functionally graded anisotropic composite structures using homogenization design method

机译:使用均质设计方法拓扑优化功能梯距各向异性复合结构

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This paper proposes a topology optimization method that is capable of simultaneous design for structural topology, stiff material (i.e. fiber) layout, and its orientations in functionally graded anisotropic composite structures. Functionally graded composites are inhomogeneous materials with continuously varying spatial composition. The spatial variation in material properties might enable better performance than an isotropic multi-material structure or fiber-reinforced composite structure with fixed fiber volume fraction. In order to enable the simultaneous design of composite topology, spatially varying fiber material layout and orientation, a homogenization design method (HDM) is applied together with a solid isotropic material with penalization (SIMP) method taking into account the advantage of each method. The SIMP method is efficient in determining discrete material states while avoiding intermediate states. Thus, it is applied to determine whether a material is void or a composite state. The HDM allows intermediate material states because it considers true anisotropic composite materials. Taking this advantage, the HDM method is applied to optimize spatially varying anisotropic fiber material layout and orientation. The optimization result of the fiber material is visualized using the projection method proposed for periodic composite structures. To validate the effectiveness of the proposed method, numerical examples for compliance minimization problems are provided. (C) 2020 Elsevier B.V. All rights reserved.
机译:本文提出了一种拓扑优化方法,其能够同时设计用于结构拓扑,刚性材料(即纤维)布局,其在功能梯度各向异性复合结构中的方向。功能梯度复合材料是不均匀的材料,具有连续变化的空间组合物。材料特性的空间变化可以使得能够比各向同性多材料结构或具有固定纤维体积分数的纤维增强复合结构更好的性能。为了使复合拓扑的同时设计,空间变化的纤维材料布局和取向,均质化设计方法(HDM)与惩罚(SIMP)方法一起施加良好的各向同性材料,考虑到每种方法的优势。 SIMP方法在确定离散材料状态时是有效的,同时避免中间状态。因此,应用以确定材料是否是空隙或复合状态。 HDM允许中间材料状态,因为它考虑了真正的各向异性复合材料。采用这一优势,应用HDM方法来优化空间变化的各向异性纤维材料布局和取向。使用针对周期复合结构所提出的投影方法可视化光纤材料的优化结果。为了验证所提出的方法的有效性,提供了合规性最小化问题的数值例子。 (c)2020 Elsevier B.v.保留所有权利。

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