In this paper, a new magneto-electro-elastic functionally graded Timoshenko microbeam model is developed by using the variational formulation. The new model incorporates the extended modified couple stress theory in order to describe the microstructure effect. The power-law variation through the thickness direction of the two-phase microbeams is considered. By the direct application of the derived general formulation, the static bending and free vibration behavior of the newly developed functionally graded material microbeams are analytically determined. Parametric studies qualitatively demonstrate the microstructural effect as well as the magneto-electro-elastic multi-field coupling effect. The proposed model and its classic counterpart produce significant differences for thin graded magneto-electro-elastic Timoshenko microbeams. The thinner the microbeam is, the larger the difference becomes.

In this paper, a microstructure-dependent magneto-electro-elastic functionally graded porous (MEEFGP) beam model is proposed using a variational approach. To account for the microstructure effect, the extended modified couple stress theory is incorporated in the new model. In addition, the porosity variation of the two-phase beam model through the thickness direction is also considered. The new developed model is verified in terms of its correctness with a FEM model. Based on the equations of motion and boundary conditions derived by Hamilton’s principle, the static bending and wave propagation behaviors of the new model are analytically determined. The results prove the existence of the microstructure effect and the magneto-electro-elastic multi-field coupling effect. There are significant differences between the new model and the classical model at the microscale. Moreover, the porosity also has an important influence on the mechanical properties of the new model. The results predicted by the new model can provide the theoretical basis for the design of microscale acoustic wave devices and micro-electro-mechanical systems.

A new model of non-classical phononic crystal (PC) microbeam for the elastic wave bandgap generation is provided, incorporating microstructure, piezomagnetism, piezoelectricity and temperature effects. The wave equation of a general magneto–electro–elastic (MEE) phononic crystal microbeam is derived, which recovers piezoelectric- and piezomagnetic-based counterparts as special cases. The piezomagnetic and piezoelectric materials are periodically combined to construct the PC microbeam and corresponding bandgaps are obtained by using the plane wave expansion (PWE) method. The effects of the piezomagnetism, piezoelectricity, microstructure, geometrical parameters and applied multi-fields (e.g., external electric potential, external magnetic potential, temperature change) on the bandgaps are discussed. The numerical results reveal that the bandgap frequency is raised with the presence of piezo and microstructure effects. In addition, the geometry parameters play an important role on the bandgap. Furthermore, large bandgaps can be realized by adjusting the external electric and magnetic potentials at micron scale, and lower bandgap frequency can be realized through the temperature rise at all length scales.

A deep learning neural network-assisted design strategy for programmable piezoelectric phononic crystal (PnC) beams with shunt circuits is proposed. The feasibility of integrating deep learning into the design of tunable PnCs to achieve real-time vibration isolation is demonstrated through numerical examples. The influence of shunt circuits (capacitance) on bandgaps of piezoelectric PnCs is studied by finite element (FE) simulations. The results show that the bandgap frequency and range vary with the capacitance and electrode length. Moreover, incorporating supercell structures introduces an additional bandgap, significantly expanding the tunable range of the bandgap and demonstrating that shunt circuit modifications can tailor the frequency and width of the bandgap. A suite of deep learning neural network (NN) algorithms is developed for predicting bandgaps and inversely designing PnC parameters, greatly accelerating the bandgap calculation and enabling faster inverse design than existing models. The accuracy of the NN algorithms is verified by comparing their predictions with those from FE simulations. The combination of designed PnC beams and deep learning NNs enables real-time vibration reduction and isolation. This design strategy is successfully validated in a practical scenario involving real-time vibration isolation of train rails.