Dynamic responses of functionally graded origami-enabled auxetic metamaterial sector plate induced by mechanical shock: Application of innovative machine learning algorithm

Zhang, Enzheng; Chen, Ying; Nasr, Emad Abouel

doi:10.1080/15376494.2023.2271922

Cited by 5 publications

(1 citation statement)

References 97 publications

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“…• MLP, being fully connected networks, are typically applied when the input and output are both of relatively low dimension, like design parameters and evaluation metrics. Wang et al [101], Du et al [102], Wang et al [103], Zhang et al [104] and Bronder et al [105] predicted the Poisson's ratio of their auxetic metamaterials using MLPs with their design parameters or design genes (also vectors) as inputs. Pham et al [106] developed an MLP using six design parameters to predict the natural frequency of auxetic honeycomb sandwich plates.…”

Section: Neural Network Based Modelsmentioning

confidence: 99%

A critical review on the application of machine learning in supporting auxetic metamaterial design

Zhang,

Zhao

2024

J. Phys. Mater.

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The progress of machine learning (ML) in the past years has opened up new opportunities to the design of auxetic metamaterials. However, successful implementation of ML algorithms remains challenging, particularly for complex problems such as domain performance prediction and inverse design.In this paper, we first reviewed classic auxetic designs and summarized their variants in different applications. The enormous variant design space leads to challenges using traditional design or topology optimization.Therefore, we also investigated how ML techniques can help address design challenges of auxetic metamaterials and when researchers should deploy them. The theories behind the techniques are explained, along with practical application examples from the analysed literature. The advantages and limitations of different ML algorithms are discussed and trends in the field are highlighted. Finally, two practical problems of ML-aided design, design scales and data collection are discussed.

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