用积分非局部理论描述的微尺度结构拓扑优化方法

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Jiayu Li, Quhao Li, Shutian Liu
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引用次数: 0

摘要

增材制造与拓扑优化的结合使复杂结构的制造成为可能,特别是对于微尺度结构,这可以保证高性能结构设计的实现。然而,拓扑结果往往包含与局部宏观结构特征长度相似的微观结构(多个多细胞尺度),导致基于经典理论的结构性能分析存在误差。因此,在拓扑优化中有必要考虑尺寸效应。在本文中,我们利用积分非局部理论建立了一个新的拓扑优化模型来考虑尺寸效应。该方法由包含核函数的积分本构模型组成,能够描述与远场应变相关的特定点的应力。给出了基于非局部理论的拓扑优化结构,并与基于经典介质理论的拓扑优化结构进行了比较。两种方法在材料布局上存在显著差异,突出了基于非局部理论的拓扑优化的必要性和所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Topology Optimization Method for Microscale Structures Described with Integral Nonlocal Theory

Topology Optimization Method for Microscale Structures Described with Integral Nonlocal Theory

Topology Optimization Method for Microscale Structures Described with Integral Nonlocal Theory

The integration of additive manufacturing and topology optimization makes it possible to fabricate complex configurations, especially for microscale structures, which can guarantee the realization of high-performance structural designs. However, topology results often contain microstructures (several multicellular scales) similar to the characteristic length of local macrostructures, leading to errors in structural performance analysis based on classical theories. Therefore, it is necessary to consider the size effect in topology optimization. In this paper, we establish a novel topology optimization model utilizing the integral nonlocal theory to account for the size effect. The approach consists of an integral constitutive model that incorporates a kernel function, enabling the description of stress at a specific point in relation to strain in a distant field. Topology optimization structures based on nonlocal theory are presented for some benchmark examples, and the results are compared with those based on classical medium theory. The material layout exhibits significant differences between the two approaches, highlighting the necessity of topology optimization based on nonlocal theory and the effectiveness of the proposed method.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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