Bending information of nanocomposites-reinforced microplate subjected to transient loading: Introducing physics-informed machine learning algorithm for solving the transient problem

Due to the importance of microplate structure in some related industries especially in microchips, transient dynamics analysis of microplate has got a lot of attention, recently. For this issue, in the current work, for the first time, transient dynamics analysis of microplate reinforced by graphene nanoplatelets composites (GPCs) using both mathematical modeling and physics-informed machine learning algorithm is presented. In the mathematical modeling section, the Halpin-Tsai correlation statement, and the role of mixture are used to simulate the composite structure. The microsystem is modeled using the classical and deviatoric components of the symmetric couple stress tensor. The transient dynamic loading with three different distribution patterns is studied to present the transient reaction of the presented microsystem to external shock. For solving the equations, a higher-order finite element model (HOFEM) with time-dependent nodal displacements, interpolation functions, and inverting the Laplace transform are used. In the machine learning section, the physics-informed machine learning algorithm is used to train and test the dataset of the mathematical modeling section. After training and testing, it is demonstrated that the physics-informed machine learning algorithm method with low computational cost can be used instead of the presented higher-order finite element model. The results show that, in comparison to alternative physics-informed machine learning algorithm configurations, having four hidden layers with the necessary number of neurons results in the highest prediction accuracy. Finally, some applicable suggestions for improving the stability of the nanocomposites-reinforced microplate under dynamical load will be presented for related industries.