Exploring buckling and post-buckling behavior of incompressible hyperelastic beams through innovative experimental and computational approaches

• O. Azarniya ^[1] ; A. Forooghi ^[1] ; M. V. Bidhendi ^[1] ; A. Zangoei ^[1] ; S. Naskar ^[2]
  1. [1] Tarbiat Modares University

     Tarbiat Modares University

     Irán

     
  2. [2] University of Southampton

     University of Southampton

     GB.ENG.M4.24UJ, Reino Unido

     
• Localización: Mechanics based design of structures and machines, ISSN 1539-7734, Vol. 52, Nº. 7, 2024, págs. 4791-4810
• Idioma: inglés
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• Resumen

  • The objective of this paper is to conduct a comprehensive investigation into the buckling and post-buckling behavior of hyperelastic beams through both computational and experimental means. Natural rubber is used in the construction of a beam with a square cross-section. To determine the mechanical properties of natural rubber, a uniaxial tensile test is performed in accordance with ASTM D412. In finite element modeling (FEM), the nonlinear behavior of rubber is modeled using hyperelastic theory and the Yeoh strain energy function. The Static-Riks method is also implemented using Abaqus for the analysis of nonlinear buckling. To validate the present investigation results with FEM, an experimental test of digital image correlation (DIC) is conducted. The critical buckling force obtained via numerical methods exhibits an error of nearly 5% when compared to the corresponding results obtained from experimental testing. In order to ascertain the impact of various design parameters on the buckling behavior of the system, a comprehensive parametric analysis has been conducted. The parameters studied include the cross-sectional thickness, length of the structure, eccentric loads, as well as the mechanical properties of the materials used in the system. Consistent with the FEM outcomes, the critical buckling force exhibited by the hyperelastic beam demonstrates a positive correlation with increasing levels of hardness, cross-sectional thickness, and eccentric loads. The buckling behavior of the system is adversely affected by increasing its length. To ultimately validate the precision and reliability of the model, a supervised neural network (NN) learning method is employed