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Wave propagation in randomly parameterized 2D lattices via machine learning

Tanmoy Chatterjee, Danilo Karlicic Orcid Logo, Sondipon Adhikari, Michael Friswell

Composite Structures, Volume: 275, Start page: 114386

Swansea University Authors: Tanmoy Chatterjee, Danilo Karlicic Orcid Logo, Sondipon Adhikari, Michael Friswell

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DOI (Published version): 10.1016/j.compstruct.2021.114386

Abstract

Periodic structures attenuate wave propagation in a specified frequency range, such that a desired bandgap behaviour can be obtained. Most periodic structures are produced by additive manufacturing. However, it is recently found that the variability in the manufacturing processes can lead to a signi...

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Published in: Composite Structures
ISSN: 0263-8223
Published: Elsevier BV 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa57610
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Most periodic structures are produced by additive manufacturing. However, it is recently found that the variability in the manufacturing processes can lead to a significant deviation from the desired behaviour. This paper investigates the elastic wave propagation of stochastic hexagonal periodic lattice structures considering micro-structural variability. Thus, the effect of uncertainties in the material and geometrical parameters of the unit cell is quantified on the wave propagation in hexagonal lattices. Based on Bloch&#x2019;s theorem and the finite element method, the band structures are determined as a function of the frequency and wave vector at the unit cell level and later scaled-up via full-scale simulations of finite metamaterials with a prescribed number of cells. State of the practice machine learning techniques, namely the Gaussian process, multi-layer perceptron, radial basis neural network and support vector machine, are employed as grey-box meta-models to capture the stochastic wave propagation response. The results demonstrate good accuracy by validation with Monte Carlo simulations. The study illustrates that considering the effect of uncertainties on the wave propagation behaviour of hexagonal periodic lattices is critical for their practical applicability and desirable performance. 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All article content, except where otherwise noted, is licensed under a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND)</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by-nc-nd/2.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2021-09-08T11:24:06.5151670 v2 57610 2021-08-13 Wave propagation in randomly parameterized 2D lattices via machine learning 5e637da3a34c6e97e2b744c2120db04d Tanmoy Chatterjee Tanmoy Chatterjee true false d99ee591771c238aab350833247c8eb9 0000-0002-7547-9293 Danilo Karlicic Danilo Karlicic true false 4ea84d67c4e414f5ccbd7593a40f04d3 Sondipon Adhikari Sondipon Adhikari true false 5894777b8f9c6e64bde3568d68078d40 Michael Friswell Michael Friswell true false 2021-08-13 FGSEN Periodic structures attenuate wave propagation in a specified frequency range, such that a desired bandgap behaviour can be obtained. Most periodic structures are produced by additive manufacturing. However, it is recently found that the variability in the manufacturing processes can lead to a significant deviation from the desired behaviour. This paper investigates the elastic wave propagation of stochastic hexagonal periodic lattice structures considering micro-structural variability. Thus, the effect of uncertainties in the material and geometrical parameters of the unit cell is quantified on the wave propagation in hexagonal lattices. Based on Bloch’s theorem and the finite element method, the band structures are determined as a function of the frequency and wave vector at the unit cell level and later scaled-up via full-scale simulations of finite metamaterials with a prescribed number of cells. State of the practice machine learning techniques, namely the Gaussian process, multi-layer perceptron, radial basis neural network and support vector machine, are employed as grey-box meta-models to capture the stochastic wave propagation response. The results demonstrate good accuracy by validation with Monte Carlo simulations. The study illustrates that considering the effect of uncertainties on the wave propagation behaviour of hexagonal periodic lattices is critical for their practical applicability and desirable performance. Based on the results, the manufacturing tolerances of the hexagonal lattices can be obtained to attain a bandgap within a certain frequency band. Journal Article Composite Structures 275 114386 Elsevier BV 0263-8223 Manufacturing variability, Hexagonal lattice, Wave propagation, Machine learning, Bloch theorem 1 11 2021 2021-11-01 10.1016/j.compstruct.2021.114386 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University 2021-09-08T11:24:06.5151670 2021-08-13T09:46:34.7500078 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Tanmoy Chatterjee 1 Danilo Karlicic 0000-0002-7547-9293 2 Sondipon Adhikari 3 Michael Friswell 4 57610__20677__3e377f137bec4bf5afcb4bb671eca9a1.pdf 57610.pdf 2021-08-19T14:57:35.1062520 Output 3977709 application/pdf Accepted Manuscript true 2022-07-29T00:00:00.0000000 ©2021 All rights reserved. All article content, except where otherwise noted, is licensed under a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND) true eng https://creativecommons.org/licenses/by-nc-nd/2.0/
title Wave propagation in randomly parameterized 2D lattices via machine learning
spellingShingle Wave propagation in randomly parameterized 2D lattices via machine learning
Tanmoy Chatterjee
Danilo Karlicic
Sondipon Adhikari
Michael Friswell
title_short Wave propagation in randomly parameterized 2D lattices via machine learning
title_full Wave propagation in randomly parameterized 2D lattices via machine learning
title_fullStr Wave propagation in randomly parameterized 2D lattices via machine learning
title_full_unstemmed Wave propagation in randomly parameterized 2D lattices via machine learning
title_sort Wave propagation in randomly parameterized 2D lattices via machine learning
author_id_str_mv 5e637da3a34c6e97e2b744c2120db04d
d99ee591771c238aab350833247c8eb9
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5894777b8f9c6e64bde3568d68078d40
author_id_fullname_str_mv 5e637da3a34c6e97e2b744c2120db04d_***_Tanmoy Chatterjee
d99ee591771c238aab350833247c8eb9_***_Danilo Karlicic
4ea84d67c4e414f5ccbd7593a40f04d3_***_Sondipon Adhikari
5894777b8f9c6e64bde3568d68078d40_***_Michael Friswell
author Tanmoy Chatterjee
Danilo Karlicic
Sondipon Adhikari
Michael Friswell
author2 Tanmoy Chatterjee
Danilo Karlicic
Sondipon Adhikari
Michael Friswell
format Journal article
container_title Composite Structures
container_volume 275
container_start_page 114386
publishDate 2021
institution Swansea University
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doi_str_mv 10.1016/j.compstruct.2021.114386
publisher Elsevier BV
college_str Faculty of Science and Engineering
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hierarchy_top_title Faculty of Science and Engineering
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hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
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description Periodic structures attenuate wave propagation in a specified frequency range, such that a desired bandgap behaviour can be obtained. Most periodic structures are produced by additive manufacturing. However, it is recently found that the variability in the manufacturing processes can lead to a significant deviation from the desired behaviour. This paper investigates the elastic wave propagation of stochastic hexagonal periodic lattice structures considering micro-structural variability. Thus, the effect of uncertainties in the material and geometrical parameters of the unit cell is quantified on the wave propagation in hexagonal lattices. Based on Bloch’s theorem and the finite element method, the band structures are determined as a function of the frequency and wave vector at the unit cell level and later scaled-up via full-scale simulations of finite metamaterials with a prescribed number of cells. State of the practice machine learning techniques, namely the Gaussian process, multi-layer perceptron, radial basis neural network and support vector machine, are employed as grey-box meta-models to capture the stochastic wave propagation response. The results demonstrate good accuracy by validation with Monte Carlo simulations. The study illustrates that considering the effect of uncertainties on the wave propagation behaviour of hexagonal periodic lattices is critical for their practical applicability and desirable performance. Based on the results, the manufacturing tolerances of the hexagonal lattices can be obtained to attain a bandgap within a certain frequency band.
published_date 2021-11-01T04:13:29Z
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score 11.012678

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