On the electromechanical energy absorption of the reinforced composites piezoelectric MEMS via Adaptive neuro-fuzzy inference system and MCS theory

Abstract

Capability of energy absorption in sandwich curved high-order beam structures is investigated in this study. The structure is composed of two surrounding piezoelectric layers with a composite core on a viscoelastic foundation. The composite core is made of carbon nanotube reinforced epoxy with 3 different patterns. Equivalent composite material properties are obtained utilizing Halpin-Tsai approach. Moreover, scale effects in this nano-composite is introduced using modified coupled stress theory (MCS) and the governing equations are derived employing Hamilton's principle. Generalized differential quadrature method (GDQM) along with Newmark-beta are used as a high performance and suitable method to numerically obtain time response of the system. In addition, artificial neural network is employed to overcome the complexity in formulation and solving differential equations with extremely lower computational costs. Utilization of ANN requires a valid dataset from experimental or numerical analyses. This dataset is collected from the numerical results in this study. The results are validated by comparing outcomes of the vibrational and damping responses of the present structure with results of a published study in this field. Afterwards, a detailed displacement–time analysis is presented for the current structure. In addition, the ANN shows capability of presenting high accuracy results to predict the amplitude, damping and frequency responses of the current structure in new loading and boundary conditions.