Application of graphene origami metamaterials to improve nonlinear vibrations of the cars' hood door under transient loading: Introducing machine learning method for solving the nonlinear problem

Created by W.Langdon from gp-bibliography.bib Revision:1.8414

@Article{Han:2024:mtcomm,

• author = "Tianlong Han and Yijie Tong and Yalin Yan and Kai Kang and Adham E. Ragab",
• title = "Application of graphene origami metamaterials to improve nonlinear vibrations of the cars' hood door under transient loading: Introducing machine learning method for solving the nonlinear problem",
• journal = "Materials Today Communications",
• year = "2024",
• volume = "39",
• pages = "109278",
• keywords = "genetic algorithms, genetic programming, GOEAM, Car's hood door, Mechanical excitation, Nonlinear vibrations, Hybrid machine learning method",
• ISSN = "2352-4928",
• URL = "https://www.sciencedirect.com/science/article/pii/S2352492824012595",
• DOI = "doi:10.1016/j.mtcomm.2024.109278",
• abstract = "Due to the importance of the car's hood door for automobiles and other related industries, in this work for the first time, some recommendations are made to enhance the nonlinear vibrations of this kind of engineering structure under external excitation. One of the recommendations is related to the used material for the fabrication of this kind of applicable structure. Functionally graded (FG) graphene origami (GOri)-enabled auxetic metamaterial (GOEAM) systems exhibit significant promise for many engineering applications owing to their remarkable physical and mechanical characteristics, including a high ratio of strength to weight, adjustable stiffness and strength, and a negative Poisson's ratio (NPR). So, due to the importance of this kind of applicable structure made of GOEAM, in this work, both hybrid machine learning algorithms and mathematical modelling are used to correctly simulate the presented nonlinear system. In the category of mathematical simulation, genetic programming is used to correctly simulate the material properties of the structure. Nonlinear strain-displacement components, Hamilton's principle, numerical solution procedure, and homotopy perturbation method are used to model and extract the responses of the current system in the mathematics domain. After obtaining the datasets using mathematics, these data are used for testing, training, and validating the results of the presented hybrid machine learning method. Finally, some recommendations for improving the efficiency and stability of the car's hood door in various situations are presented in detail",

}

Genetic Programming entries for Tianlong Han Yijie Tong Yalin Yan Kai Kang Adham E Ragab

Citations