Abstract—In this research, we discuss how we extract features related to natural frequency of Nonlinear Composite Plates (NCP) using a combination of Wavelet Transform (WT) and Artificial Intelligence (AI) algorithms. The nonlinearity is represented in the plate geometry and Boundary Conditions (BCs). Our findings, which build on previous works by the authors, indicate that WT can effectively reflect the natural frequency features of NCP. However, we also noted that this approach can be quite complex, involving numerous calculations and iterations, which means it might not be ideal for quickly and accurately extracting natural frequency. To tackle this issue, we’ve developed a new AI model designed to learn from and test these results by extracting key natural frequency features that reveal crucial information about the NCP behavior. The AI model is based on a Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM) blocks since the natural frequency datasets have a time-dependent and memory-dependent behavior. Our results strongly suggest that the proposed technique is a promising approach, especially for complex structures in varying environmental conditions.

Keywords—Nonlinear Composite Plates (NCP), natural frequency, Wavelet Transform (WT), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) blocks

Cite: Wael A. Altabey, "The Nonlinear Study of Composite Plates Natural Frequency Using RNN-LSTM combined with Wavelet Transform Features," International Journal of Mechanical Engineering and Robotics Research, Vol. 15, No. 2, pp. 197-210, 2026. doi: 10.18178/ijmerr.15.2.197-210

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).