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T. Park, D. Noh, J. Park, J. Lee, S. Park, W. Choi, G. Noh, "Design of Tuneable Multifunctioning Metamaterial Absorbers using Progressive Neural Network Metaheuristics", in P. Iványi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Twelfth International Conference on Engineering Computational Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 8, Paper 4.2, 2024, doi:10.4203/ccc.8.4.2

Keywords: octet-truss, multifunctioning metamaterials, progressive sampling, neural networks, metaheuristics, finite element analysis.

Abstract

This paper presents a novel design optimization framework for a tuneable broadband mechanical metamaterial absorber (TBMMA). Utilizing a progressive neural network and metaheuristic algorithms, we capture the complex behaviour behind the mechanical properties and electromagnetic properties and enhance both microwave absorption and stiffness of octet-truss based metamaterial absorber. The proposed method leverages a multilayered octet truss structure with functionally graded approach, adjusting design variables such as strut diameter and aspect ratio to achieve optimal performance. This integrated approach demonstrates significant improvements in multifunctional properties with tunability through various optimization problems differed by operating frequency range, constraints for target design.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 8 PROCEEDINGS OF THE TWELFTH INTERNATIONAL CONFERENCE ON ENGINEERING COMPUTATIONAL TECHNOLOGY Edited by: P. Iványi, J. Kruis and B.H.V. Topping Paper 4.2 Design of Tuneable Multifunctioning Metamaterial Absorbers using Progressive Neural Network Metaheuristics T. Park, D. Noh, J. Park, J. Lee, S. Park, W. Choi and G. Noh School of Mechanical Engineering, Korea University, Seoul, South Korea doi:10.4203/ccc.8.4.2 Full Bibliographic Reference for this paper T. Park, D. Noh, J. Park, J. Lee, S. Park, W. Choi, G. Noh, "Design of Tuneable Multifunctioning Metamaterial Absorbers using Progressive Neural Network Metaheuristics", in P. Iványi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Twelfth International Conference on Engineering Computational Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 8, Paper 4.2, 2024, doi:10.4203/ccc.8.4.2 Keywords: octet-truss, multifunctioning metamaterials, progressive sampling, neural networks, metaheuristics, finite element analysis. Abstract This paper presents a novel design optimization framework for a tuneable broadband mechanical metamaterial absorber (TBMMA). Utilizing a progressive neural network and metaheuristic algorithms, we capture the complex behaviour behind the mechanical properties and electromagnetic properties and enhance both microwave absorption and stiffness of octet-truss based metamaterial absorber. The proposed method leverages a multilayered octet truss structure with functionally graded approach, adjusting design variables such as strut diameter and aspect ratio to achieve optimal performance. This integrated approach demonstrates significant improvements in multifunctional properties with tunability through various optimization problems differed by operating frequency range, constraints for target design. download the full-text of this paper (PDF, 1274 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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