An artificial neural network model for multi-flexoelectric actuation of Plates

Abstract

ABSTRACT Flexoelectric effect can be used to design actuators to control engineering structures including beams, plates, and shells. Multiple flexoelectric actuators method has the advantage of less stress concentration and better control effect, but the mode-dependent optimal actuator locations could influence the flexoelectric actuation effect significantly. In this work, a neural network model is established to study the optimal combinations of multiple flexoelectric actuators on a rectangular plate. In the physical model, an atomic force microscope (AFM) probe was employed to generate an electric field gradient in the flexoelectric patch, so that flexoelectric control force and moment can be obtained. Multiple flexoelectric actuators on the plate was considered. Case studies showed that the flexoelectricity induced stress mainly concentrate near the probe, the size and shape of the flexoelectric patch have limited effect on the actuation, hence, only the actuator positions were choosing as the input of the ANN model. Using the prediction of the neural network model, the driving effect of a large number of actuators at different positions can be quickly obtained, and the optimal position of the actuator can be analyzed more accurately.