Design of a Resonator-Based Metamaterial for Broadband Control of Transverse Cable Vibration

Singleton, Lawrence; Cheer, Jordan; Daley, S.

doi:10.18154/rwth-conv-239432

Cited by 3 publications

(4 citation statements)

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“…The objective function was then described as the difference between the desired output and the optical response of a meta-atom, and gradient descent optimization was used to seek out and find the most suitable meta-geometries. Other papers in the field employ algorithms such as gradient descent to optimize problems related to elastic metamaterial-based vibration absorbers [100], electromagnetic devices [101], photonic band gap structures [102] and acoustic metamaterials [103]. Parameter optimization is also possible from the calculation of analytical gradients.…”

Section: Gradient-based Methodsmentioning

confidence: 99%

“…Application field: Electromagnetics Sakurai, Yada, Simomura, et al [327] 2019 Bayesian optimization optimization framework Pita Ruiz, Amad, Gabrielli, et al [101] 2019 Gradient-descent and Opological optimization framework -derivative-based optimization Kurniawati, Putri, and Ningsih [123] 2020 Random forest regression optimization framework Zhang, Wang, Xu, et al [328] 2022 Bayesian Optimization Optimization framework Chuma and Rasmussen [329] 2022 KNN,SVM,Bayesian Optimization Optimization framework Jian, Alexandropoulos, Basar, et al [330] 2022 KNN,SVM,Bayesian Optimization Optimization framework Alharbi, Abdelhamid, Ibrahim, et al [331] 2023 KNN,SVM,Gradient-based Optimization Optimization framework Lin, Zheng, Hu, et al [332] 2023 Bayesian Optimization Optimization framework Application field: Mechanical Morris and Seepersad [333] 2018 Spectral clustering Classification and clustering Bessa, Glowacki, and Houlder [130] 2019 Bayesian ML Classification and clustering Singleton, Cheer, and Daley [100] 2019 Gradient-descent optimization framework Dong, Chen, Zeng, et al [129] 2019 Nelder-Mead optimization optimization framework Stern, Arinze, Perez, et al [334] 2020 Nonlinear programming optimization framework Liu, Ye, Silva Izquierdo, et al [335] 2022 SVM classification Prasanna, Shantha, Pradeep, et al [336] 2022 SVM ,KNN Optimization Framework Zhai and Yeo [337] 2023 Bayesian Learning inverse design Hu, Wang, Du, et al [338] 2023 Bayesian Optimization inverse design Hu, Zhan, Wang, et al [339] 2023 LS-SVM Optimization Framework…”

Section: The Causal Relationship Problemmentioning

confidence: 99%

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Machine Intelligence in Metamaterials Design

Cerniauskas,

Sadia,

Alam

2023

Preprint

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Machine intelligence continues to rise in popularity as an aid to the design and discovery of novel metamaterials. The properties of metamaterials are essentially controllable via their architectures and until recently, the design process has relied on a combination of trial-and-error and physics-based methods for optimization. These processes can be time-consuming and challenging, especially if the design space for metamaterial optimization is explored thoroughly. Artificial intelligence (AI) and machine learning (ML) can be used to overcome challenges like these as pre-processed massive metamaterial datasets can be used to very accurately train appropriate models. The models can be broad, describing properties, structure, and function at numerous levels of hierarchy, using relevant inputted knowledge. Here, we present a comprehensive review of the literature where state-of-the-art machine intelligence is used for the design, discovery and development of metamaterials. In this review, individual approaches are categorized based on methodology and application. We further present machine intelligence trends over a wide range of metamaterial design problems including: acoustics, photonics, plasmonics, mechanics, and more. Finally, we identify and discuss recent research directions and highlight current gaps in knowledge. KeywordsMetamaterials • Artificial intelligence • Machine learning • Neural Networks • Evolutionary algorithms • Surrogate modelling • Optimization 42 solely related to the properties of material constituents. 43 Properties can therefore be controlled and manipulated by 44 changing metamaterial 'unit cell' topology and while the 45 bulk properties of the constituent materials have influence, 46 these properties are often not the sole dominating variable 47 used in designing metamaterials. When designing with 48 respect to topology, the properties of metamaterials can 49 be customized within a very large design space, and this 50 is one of the reasons for the exponential growth in meta-51 materials R&D across the breadth of modern engineering.

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Section: Gradient-based Methodsmentioning

confidence: 99%

Section: The Causal Relationship Problemmentioning

confidence: 99%

Machine Intelligence in Metamaterials Design

Cerniauskas,

Sadia,

Alam

2023

Preprint

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“…Meta-structure optimisation methods have previously employed finite element analysis (FEA) as a basis for structure-property enhancements [16,17]. These include non-linear programming [18], gradient-descent [19,20,21], Bayesian optimisation [22,23], deep learning [24,25] and various evolutionary algorithms [26,27,28,29,30] as a basis for the optimisation frameworks. These optimisation frameworks rely on topology [31,3,17,25] and parametric design approaches [22,26,32,33,34,5] to alter the arrangement of metamaterial lattices [23,4], chiral structures [34,32] and thin-walled cellular solids [6,7,8].…”

Section: Introductionmentioning

confidence: 99%

Compressive properties of parametrically optimised mechanical metamaterials based on 3D projections of 4D geometries

Cerniauskas¹,

Alam²

2023

Preprint

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The design process of 3D mechanical metamaterials is still an emerging field and in this paper, we propose for the first time, a new design and optimisation approach based on 3D projections of 4D geometries (4-polytopes) and evolutionary algorithms. We find that through iterative parametric optimisation, 4-polytope projected mechanical metamaterials can be optimised to achieve both high specific stiffness and high specific yield strengths. Samples manufactured using a low-stereolithography method were tested in compression. We find that optimised tesseracts (8-cell structures) had a higher specific yield strength (22.8 kNm/kg) than that of honeycomb structures tested out-ofplane (19.4 kNm/kg) and a specific stiffness of (0.68 MNm/kg) which is more than 3-fold that of gyroid structures. The compressive strength to solid-modulus ratio of the 8-cell tesseract is very high (3×10 −3 ), exceeding that of out-ofplane honeycombs, which are themselves closer in value to 5-cell pentatopes (2×10 −3 ). 8-cell and 5-cell structures are in the region of one order of magnitude higher than 16-cell and 24-cell structures (∼ 2 × 10 −4 − 8 × 10 −4 ) and are hence comparable to nanostructured metamaterials. The 8-cell tesseracts are 18% stiffer, 43% stronger, and 19% tougher in compression than out-of-plane honeycomb structures, but unlike honeycombs, 8-cell tesseracts are 3D structures with cubic symmetry. Architecture has a profound effect on the relative consistency of properties with cubically symmetric structures displaying the greatest levels of consistency in terms of both strength and stiffness reduction as a function of pore space. The results presented in this paper showcase the potential of this new class of mechanical metamaterial based on 3D projected 4-polytopes.

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“…Advanced optimized methods use meta-heuristic methods and machine learning algorithms, to aid the exploration of the metamaterial design space by either carrying out parametric optimization [26,27] of the metamaterial structures, or by enabling the exploration of inverse design approaches [7,26]. Such approaches include evolutionary algorithms [28,29], as well as Bayesian optimization [15,27], gradient-descent [30,31,32] and neural-network-powered techniques [33,7,26].…”

Section: Introductionmentioning

confidence: 99%

Tensile properties of 3D projected 4-polytopes: a new class of mechanical metamaterial

Cerniauskas¹,

Alam²

2023

Preprint

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In this paper, we explore the mechanical behavior of a new class of mechanical metamaterials based on the 3D projections of 4-dimensional geometries (4-polytopes) subjected to loading in tension. We demonstrate that the specific properties of mechanical metamaterials can be enhanced by more than 4-fold when optimized within a framework powered by an evolutionary algorithm. Optimized metamaterial structures were manufactured using the low-forcestereolithography prototyping technique and mechanically tested in tension. The experimental results show that the best-performing metamaterial structure, the 8-cell (tesseract), has specific yield strength and specific stiffness values in a similar range to those of hexagonal honeycombs tested out-of-plane. Nevertheless, the 8-cell structures are also cubically symmetrical and have the same mechanical properties in three orthogonal axes. The effect of structure is quantified by comparing the tensile strength against the Young's modulus of bulk solid material. We find that the final value of the 8-cell structures exceeds that of the hexagonal honeycomb by 76%. The 5-cell (pentatope) and 16-cell (orthoplex) metamaterials are shown to be more effective in bearing tensile loads than the gyroid structures, while the 24-cell (octaplex) structures exhibit the lowest ratio and possess the least optimal structure-properties relationships. The findings presented in this paper showcase the importance of macro-scale architecture and highlight the potential of 3D projections of 4-polytopes as the basis for a new class of mechanical metamaterials.

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Design of a Resonator-Based Metamaterial for Broadband Control of Transverse Cable Vibration

Cited by 3 publications

References 0 publications

Machine Intelligence in Metamaterials Design

Machine Intelligence in Metamaterials Design

Compressive properties of parametrically optimised mechanical metamaterials based on 3D projections of 4D geometries

Tensile properties of 3D projected 4-polytopes: a new class of mechanical metamaterial

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