Machine learning assisted coupled frequency analysis of skewed multi-phase magnetoelectric composite plates with temperature and moisture dependent properties

Abstract

In this article, the application of an artificial neural network (ANN)-based machine learning (ML) strategy to predict the coupled frequency of geometrically skewed multiphase magnetoelectric (MME) composite plate exposed to hygrothermal environment is presented. The ANN model is trained using a dataset comprising more than one million simulations conducted using an in-house developed finite element formulation. The underlying multiphysics governing equations are derived using Hamilton’s principle and higher-order shear deformation theory (HSDT). The influence of the hygrothermal environment on the elastic stiffness of MME composites is defined by the empirical constants in the constitutive relations. Four prominent combinations of the empirical constants leading to different elastic stiffness relations have been considered in this study. Alongside, the influence of geometrical skewness on the coupled fundamental frequency is also assessed. For the training of the ANN model, the Levenberg–Marquardt optimization algorithm with 30 neurons along with a tangent sigmoid activation function is used. The trained ANN model is tested on an unseen dataset, different from the training data, and it is shown to accurately predict the natural frequency of MME plate with a maximum error of 2.3%. Further, excluding the training time and considering the computational time alone, the ANN model is found to be 6.3 times faster than the FE simulations. It is anticipated that such ML-based reduced order models can be effective in the design process, especially in complex multiphysics problems, such as the one considered in the work, involving a multitude of geometric, loading and material parameters.