Concurrent topology optimization of multiscale composite structures in Matlab

Gao, Jie; Luo, Zhen; Xia, Liang; Gao, Liang

doi:10.1007/s00158-019-02323-6

Cited by 119 publications

(66 citation statements)

References 77 publications

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“…Moreover, this approach can be further extended to a multi-scale problem where the microstructures are optimized concurrently with the global geometry instead of selecting from a set of predetermined lattices. [43][44][45] Additionally, with the help of advanced AM technologies (e.g., inkjet and polyjet), composites with intricate material distributions can be created and much success has been shown in the bioinspired materials community. 46,47 Taking advantage of this multi-material fabrication ability, researchers have modified the density-based approach so that the design parameter h describes the similarity of the representative volume element to the composite constituents or to void.…”

Section: Geometrical Designmentioning

confidence: 99%

Machine Learning for Advanced Additive Manufacturing

Jin

Zhang

Demir

et al. 2020

Matter

154

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Section: Geometrical Designmentioning

confidence: 99%

Machine Learning for Advanced Additive Manufacturing

Jin

Zhang

Demir

et al. 2020

Matter

154

View full text Add to dashboard Cite

“…The extremal point properties afforded by bespoke periodic microstructures within multiscale optimization frameworks (Rodrigues et al 2002;Schumacher et al 2015;Sivapuram et al 2016;Zhu et al 2017;Li et al 2018;Watts et al 2019;Garner et al 2019;Gao et al 2019) represent an advantage over topology optimization (Bendsøe and Sigmund 2004), which is constrained to isotropic point material properties. This is an advantage shared by free material optimization (Kočvara et al 2002(Kočvara et al , 2008, which uses all 21 unique elements of the elasticity tensor as design variables to derive theoretically optimal but nonmanufacturable structures.…”

Section: Introductionmentioning

confidence: 99%

Multiscale structural optimization with concurrent coupling between scales

Murphy

Imediegwu

Hewson

et al. 2021

Struct Multidisc Optim

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A robust three-dimensional multiscale structural optimization framework with concurrent coupling between scales is presented. Concurrent coupling ensures that only the microscale data required to evaluate the macroscale model during each iteration of optimization is collected and results in considerable computational savings. This represents the principal novelty of this framework and permits a previously intractable number of design variables to be used in the parametrization of the microscale geometry, which in turn enables accessibility to a greater range of extremal point properties during optimization. Additionally, the microscale data collected during optimization is stored in a reusable database, further reducing the computational expense of optimization. Application of this methodology enables structures with precise functionally graded mechanical properties over two scales to be derived, which satisfy one or multiple functional objectives. Two classical compliance minimization problems are solved within this paper and benchmarked against a Solid Isotropic Material with Penalization (SIMP)–based topology optimization. Only a small fraction of the microstructure database is required to derive the optimized multiscale solutions, which demonstrates a significant reduction in the computational expense of optimization in comparison to contemporary sequential frameworks. In addition, both cases demonstrate a significant reduction in the compliance functional in comparison to the equivalent SIMP-based optimizations.

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“…In this branch, the Level Set Method (LSM) [15][16][17], the phase field method [18,19], the recently proposed Moving Morphable Components/Voids (MMC/V) method [20][21][22][23] and the Bubble method [24,25] have been obtained considerable discussions. These developed TO methods have been also applied to address several different numerical problems, like the dynamic optimization [26][27][28], compliant mechanisms [29,30], stress problems [31][32][33], robust designs [34][35][36], materials design [37][38][39][40][41], concurrent topology optimization [42][43][44][45][46][47][48].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Isogeometric Topology Optimization: Methods, Applications and Prospects

Gao

Xiao

Zhang

et al. 2020

Chin. J. Mech. Eng.

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Topology Optimization (TO) is a powerful numerical technique to determine the optimal material layout in a design domain, which has accepted considerable developments in recent years. The classic Finite Element Method (FEM) is applied to compute the unknown structural responses in TO. However, several numerical deficiencies of the FEM significantly influence the effectiveness and efficiency of TO. In order to eliminate the negative influence of the FEM on TO, IsoGeometric Analysis (IGA) has become a promising alternative due to its unique feature that the Computer-Aided Design (CAD) model and Computer-Aided Engineering (CAE) model can be unified into a same mathematical model. In the paper, the main intention is to provide a comprehensive overview for the developments of Isogeometric Topology Optimization (ITO) in methods and applications. Finally, some prospects for the developments of ITO in the future are also presented.

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Concurrent topology optimization of multiscale composite structures in Matlab

Cited by 119 publications

References 77 publications

Machine Learning for Advanced Additive Manufacturing

Machine Learning for Advanced Additive Manufacturing

Multiscale structural optimization with concurrent coupling between scales

A Comprehensive Review of Isogeometric Topology Optimization: Methods, Applications and Prospects

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