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<p>In this paper, the vibration analysis of three-phase composite plate with polymer matrix reinforced with fiber and particle components is investigated. Initially, the effective material properties of the three-phase composite material are derived analytically, considering nonlinear dependencies on the volume fractions of constituent materials and their interactions. Subsequently, the governing equations of motion are formulated using Reddy’s higher-order shear deformation plate theory and solved by applying the Galerkin method combined with the Runge–Kutta method to obtain the vibration characteristics. Further<b>,</b> optimization is performed using the bees algorithm to maximize the natural frequencies with respect to four material variables within specified ranges. Moreover, an artificial neural network (ANN) model is developed and trained on a dataset of 1144 analytical records to efficiently predict the natural frequencies. Finally, numerical results are presented to highlight the influences of reinforcement fibers and particles on the vibration behaviors, showing that increasing the fiber volume fraction leads to higher natural frequencies and lower deflection amplitudes, whereas increasing the particle volume fraction results in reductions in both the natural frequency and the deflection amplitude, thereby providing practical guidelines for manufacturers in selecting suitable material proportions for structural and industrial applications.</p>

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Analytical and ANN-based approaches for vibration analysis and material optimization of three-phase composite plate

  • 期刊论文

  • 发表日期：

    2026年3月27日

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  • Dinh Van Dat,
  • Nguyen Van Duy,
  • Tran Quoc Quan,
  • Nguyen Dinh Duc

摘要

In this paper, the vibration analysis of three-phase composite plate with polymer matrix reinforced with fiber and particle components is investigated. Initially, the effective material properties of the three-phase composite material are derived analytically, considering nonlinear dependencies on the volume fractions of constituent materials and their interactions. Subsequently, the governing equations of motion are formulated using Reddy’s higher-order shear deformation plate theory and solved by applying the Galerkin method combined with the Runge–Kutta method to obtain the vibration characteristics. Further, optimization is performed using the bees algorithm to maximize the natural frequencies with respect to four material variables within specified ranges. Moreover, an artificial neural network (ANN) model is developed and trained on a dataset of 1144 analytical records to efficiently predict the natural frequencies. Finally, numerical results are presented to highlight the influences of reinforcement fibers and particles on the vibration behaviors, showing that increasing the fiber volume fraction leads to higher natural frequencies and lower deflection amplitudes, whereas increasing the particle volume fraction results in reductions in both the natural frequency and the deflection amplitude, thereby providing practical guidelines for manufacturers in selecting suitable material proportions for structural and industrial applications.