A novelty solution for orthotropic composite plate based on physics informed neural network

Abstract

The modeling of cross-ply composite laminates using numerical methods has been a difficult task, leading to the development of various finite element method and other analytical solutions. However, as materials science advances, this problem has become more complex, presenting new challenges that require reliable and novel approaches. In this study, we propose the utilization of machine learning, specifically physics informed neural networks (PINN), for the first time to examine the behavior of composite plate. By solving a system of partial differential equations derived from the virtual work equilibrium principle, PINN are employed as a method to solve these equations using a generalized strong-form approach. To address the issue of imbalanced loss functions, we also propose adjusting the loss function in this research. Once trained, PINN serve as a surrogate model capable of predicting displacements and stresses in cross-ply composite laminates. To demonstrate the effectiveness and reliability of PINN, we investigate two examples of laminates with different material distributions and boundary conditions including boundary conditions on displacement and boundary conditions on stress, comparing the results with the benchmark Navier solution. The research and obtained results showcase the performance and accuracy of PINN, highlighting their potential as a surrogate model for solving problems related to cross-ply composite laminates.

Graphical abstract

Keywords

PINN / loss function / laminate plates / Navier solution

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