De-homogenization of optimal 2D topologies for multiple loading cases

Jensen, Peter Dørffler Ladegaard; Sigmund, Ole; Groen, Jeroen

doi:10.1016/j.cma.2022.115426

Cited by 15 publications

(8 citation statements)

References 42 publications

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“…One way to overcome this problem and make the de-homogenized structures more stable and independent of the cell size and position is to close the microstructure at void interfaces. An approach using a shell with constant thickness was suggested in [41]. In the present work we want an addaptive shell thicknes, thus we use erosion-based interface detection [42] with a local erosion threshold parameter ∆η j where w j is the width of the shell and R shell is the filter radius in the standard filter.…”

Section: Closing the Boundary Shellmentioning

confidence: 99%

Topology optimization of multiscale structures considering local and global buckling response

Christensen

Wang

Sigmund

2023

Computer Methods in Applied Mechanics and Engineering

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Section: Closing the Boundary Shellmentioning

confidence: 99%

Topology optimization of multiscale structures considering local and global buckling response

Christensen

Wang

Sigmund

2023

Computer Methods in Applied Mechanics and Engineering

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“…To extend the applicability of de-homogenization, various enhanced methods have been proposed to accommodate more complicated unit cells. Ladegaard et al [292] combined three cosine fields to construct the implicit mapping functions for oriented rank-3 microstructures (Figure 20e). It enables the dehomogenization to find the optimal structures for static compliance problems in multi-loading cases.…”

Section: Variant 1: Considering Unit-cell Orientationmentioning

confidence: 99%

“…Reproduced permission. [ 292 ] f) De‐homogenized design via Fourier‐series‐based mapping. Reproduced with permission.…”

Section: Data‐driven Multiscale Metamaterials System Designmentioning

confidence: 99%

“…Ladegaard et al. [ 292 ] combined three cosine fields to construct the implicit mapping functions for oriented rank‐3 microstructures (Figure 20e). It enables the de‐homogenization to find the optimal structures for static compliance problems in multi‐loading cases.…”

Section: Data‐driven Multiscale Metamaterials System Designmentioning

confidence: 99%

“…Copyright 2018, John Wiley & Sons, Ltd. e) De-homogenized design obtained for multiloading cases using microstructures with three different layers. Reproduced permission [292]. f) De-homogenized design via Fourier-series-based mapping.…”

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confidence: 99%

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Data‐Driven Design for Metamaterials and Multiscale Systems: A Review

Lee,

Chen,

Wang

et al. 2023

Advanced Materials

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Metamaterials are artificial materials designed to exhibit effective material parameters that go beyond those found in nature. Composed of unit cells with rich designability that are assembled into multiscale systems, they hold great promise for realizing next‐generation devices with exceptional, often exotic, functionalities. However, the vast design space and intricate structure–property relationships pose significant challenges in their design. A compelling paradigm that could bring the full potential of metamaterials to fruition is emerging: data‐driven design. This review provides a holistic overview of this rapidly evolving field, emphasizing the general methodology instead of specific domains and deployment contexts. Existing research is organized into data‐driven modules, encompassing data acquisition, machine learning‐based unit cell design, and data‐driven multiscale optimization. The approaches are further categorized within each module based on shared principles, analyze and compare strengths and applicability, explore connections between different modules, and identify open research questions and opportunities.

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