Authors: Chatterjee, Tanmoy
Karličić, Danilo 
Adhikari, Sondipon
Friswell, Michael I.
Affiliations: Mechanics 
Mathematical Institute of the Serbian Academy of Sciences and Arts 
Title: Wave propagation in randomly parameterized 2D lattices via machine learning
Journal: Composite Structures
Volume: 275
First page: 114386
Issue Date: 1-Nov-2021
Rank: ~M21a
ISSN: 0263-8223
DOI: 10.1016/j.compstruct.2021.114386
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.
Keywords: Bloch theorem | Hexagonal lattice | Machine learning | Manufacturing variability | Wave propagation
Publisher: Elsevier

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