An efficient neural network approach for laminated composite plates using refined zigzag theory

Abstract

This paper presents an innovative methodology employing the One-dimensional Convolutional Gated Recurrent Unit neural network (1D-CGRU) algorithm for the analysis of laminated composites using the Refined Zigzag theory (RZT). The RZT methodology is utilized to assess laminated plate structures and generate essential data, forming the basis for training the 1D-CGRU model. The synergistic application of RZT and 1D-CGRU demonstrates exceptional global-local accuracy in predicting the mechanical behavior of laminated composite plates. For efficient data generation, RZT not only provides high precision, but also exhibits computational efficiency, making it suitable for finite element simulations with a C0-continuous kinematic approximation. Additionally, the 1D-CGRU model integrates the strengths of a One-dimensional Convolutional Neural Network (1D-CNN) for spatial feature extraction and dimensionality reduction, coupled with a Gated Recurrent Unit (GRU) network for discerning temporal relationships and mapping them to the target domain. Furthermore, quantitative accuracy measurements, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), are used to validate the superior performance of the 1D-CGRU model compared to other surrogate models. For angle-ply and cross-ply composite laminates, the 1D-CGRU model achieves remarkable accuracy (98.15% and 99.45%, respectively) with low RMSE values. These results highlight the potential of the proposed framework to enhance predictive analysis for laminated composite structures, offering valuable insights for engineering applications and design optimizations.