An improved ordered SIMP approach for multiscale concurrent topology optimization with multiple microstructures

Gu, Xuechen; He, Shaoming; Dong, Yihao; Song, Tao

doi:10.1016/j.compstruct.2022.115363

Cited by 34 publications

(10 citation statements)

References 50 publications

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“…This projection procedure regards macroscopic elements with similar density as the same type of material, and are implemented by piecewise projection method. Furthermore, the double smoothing and projection method guarantees a clearer distinction between multi-material distribution domains in comparison to the single smoothing and projection operation (referring to Luo et al (Liu et al 2020)), as reported by Gu et al (Gu et al 2022)and Groen et al (Groen et al 2019). Therefore, we introduce a double Helmholtz smoothing and piecewise projection method to filter the preliminary design The connectivity issue of the microstructures is necessary to be considered for improving the manufacturability of the optimized two-scale designs.…”

Section: Connectable Lattice Structurementioning

confidence: 96%

“…To retain a tradeoff between design space and computational burden, there exist two dominant philosophies proposed in recent years. The first is to split the macroscopic design domain to multiple sub-domains configurated by various microstructures on the basis of the artificial prescription (Liu et al 2019;Zhang et al 2021), multi-material domains by DMO or ordered SIMP model (Liu et al 2020;Luo et al 2021;Gu et al 2022;)and principal stress (Xu and Cheng 2018;Qiu et al 2021). Zhang et al (Zhang et al 2021)proposed a multi-scale topology optimization method for the design of composite macrostructures with multiphase viscoelastic composite microstructures for maximizing the modal loss factor.…”

Section: Introductionmentioning

confidence: 99%

“…For the interval-frequency loads existing in many engineering problems, Liu et al )developed a dynamic concurrent topology optimization framework to design layer-wise graded structures. As for interfacial connectivity between distinct microstructures, many connectivity models are introduced to enhance the manufacturability including the kinematically connective constraint (Chu et al 2020;, the nondesignable domain (Hu et al 2021;Gu et al 2022), the isogenous method (Wang et al 2017)and the designable connective region method (Duriez et al 2021;Liu et al 2020).…”

Section: Introductionmentioning

confidence: 99%

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Two-scale concurrent topology optimization of lattice structures with multiple microstructures subjected to dynamic load

jiang,

qi,

teng

2024

Preprint

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This work intends to present a two-scale concurrent topology optimization method for minimizing the compliance of lattice structures with multiple connectable microstructures under time-dependent dynamic load. Firstly, at the macroscale, the ordered solid isotropic material with penalization (SIMP) method and double smoothing and projection method is integrated to identify the macrostructural layout of any lattice material represented by a unique microstructure, i.e. optimal locations of microstructures. At the microscale, the connectivity between any pair of microstructures is guaranteed by adopting the designable connective region method. Then, for transient optimization problem, we implement the sensitivity analysis based on the adjoint method with the “discretize-then-differentiate” approach, which inherently generates consistent sensitivities. Moreover, we develop a decoupled sensitivity analysis method for transient concurrent topology optimization problems with multiple connectable microstructures for computationally efficient sensitivity analysis at the microscale. Finally, serval numerical examples are presented to verify the effectiveness and the capability of the proposed approach.

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Section: Connectable Lattice Structurementioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Two-scale concurrent topology optimization of lattice structures with multiple microstructures subjected to dynamic load

jiang,

qi,

teng

2024

Preprint

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“…Therefore, the goal of the optimization problem of topological optimization of the interconnection of multilayer composite structures is to reduce the peak shear stresses in the solder layer. Among the algorithms used for topological optimization, such as homogenization [29], SIMP [30], ESO [31], and the RAMP approach [32], SIMP and RAMP methods are the most popular. While the SIMP method implies high efficiency in a narrow range of problems, the RAMP method is a stable method that can be used in a wider class of problems [33].…”

Section: Statement Of the Topological Optimization Problemmentioning

confidence: 99%

Topological Optimization of Interconnection of Multilayer Composite Structures

et al. 2023

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This study focuses on the topological optimization of adhesive overlap joints for structures subjected to longitudinal mechanical loads. The aim is to reduce peak stresses at the joint interface of the elements. Peak stresses in such joints can lead to failure of both the joint and the structure itself. A new approach based on Rational Approximation of Material Properties (RAMP) and the Finite Element Method (FEM) has been proposed to minimize peak stresses in multi-layer composite joints. Using this approach, the Mises peak stresses of the optimal structural joint have been significantly reduced by up to 50% under mechanical loading in the longitudinal direction. The paper includes numerical examples of different types of structural element connections.

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“…However, one of the challenges in M-TO is the high computational cost since one must generate and evaluate various microstructures (through homogenization) during each step of the optimization process ( [13,21,15,26]). To reduce this cost, researchers have proposed the use of graded variations of one or more pre-selected microstructural topologies ( [14,19,27]).…”

Section: Graded Multiscale Tomentioning

confidence: 99%

GM-TOuNN: Graded Multiscale Topology Optimization using Neural Networks

Chandrasekhar¹,

Sridhara²,

Suresh³

2022

Preprint

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Multiscale topology optimization (M-TO) entails generating an optimal global topology, and an optimal set of microstructures at a smaller scale, for a physics-constrained problem. With the advent of additive manufacturing, M-TO has gained significant prominence. However, generating optimal microstructures at various locations can be computationally very expensive. As an alternate, graded multiscale topology optimization (GM-TO) has been proposed where one or more pre-selected and graded (parameterized) microstructural topologies are used to fill the domain optimally. This leads to a significant reduction in computation while retaining many of the benefits of M-TO. A successful GM-TO framework must: (1) be capable of efficiently handling numerous pre-selected microstructures, (2) be able to continuously switch between these microstructures during optimization, (3) ensure that the partition of unity is satisfied, and (4) discourage microstructure mixing at termination. In this paper, we propose to meet these requirements by exploiting the unique classification capacity of neural networks. Specifically, we propose a graded multiscale topology optimization using neural-network (GM-TOuNN) framework with the following features: (1) the number of design variables is only weakly dependent on the number of pre-selected microstructures, (2) it guarantees partition of unity while discouraging microstructure mixing, and (3) it supports automatic differentiation, thereby eliminating manual sensitivity analysis. The proposed framework is illustrated through several examples.

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An improved ordered SIMP approach for multiscale concurrent topology optimization with multiple microstructures

Cited by 34 publications

References 50 publications

Two-scale concurrent topology optimization of lattice structures with multiple microstructures subjected to dynamic load

Two-scale concurrent topology optimization of lattice structures with multiple microstructures subjected to dynamic load

Topological Optimization of Interconnection of Multilayer Composite Structures

GM-TOuNN: Graded Multiscale Topology Optimization using Neural Networks

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