Energy absorption in composite structure reinforced with advanced functionally graded nano-materials: Artificial intelligence and numerical approaches

Abstract

The energy absorption characteristics of materials and structures are crucial for ensuring the safety of structures when subjected to mechanical forces. This study focuses on the significance of energy absorption in composite structures. Specifically, it examines the energy absorption in composite structures that are reinforced using sophisticated functionally graded nano-materials. The analysis is conducted using both mathematical modeling and artificial intelligence methods. The Halpin–Tsai micromechanics method is employed to simulate the material properties of the advanced composite construction, taking into account the role of the mixture. Within the realm of mathematical modeling, the energy method, higher-order Taylor’s Formula, Viscoelastic formula, and differential quadrature method (DQM) are employed to extract and solve the governing equations of composite structures reinforced with advanced functionally graded nano-materials. The Deep Neural Networks Model (DNNM) is a highly popular supervised machine learning approach that may be employed for both regression and classification tasks. The objective of regression analysis is to determine the correlation between independent variables and dependent responses by identifying the function of optimal decision that can accurately explain the fluctuations in the response parameter depending on the dependent variables. In the second phase, the findings acquired from mathematical modeling are trained and evaluated for the aim of DNNM. Upon receiving the results, recommendations for enhancing the energy absorption in composite structures reinforced with advanced functionally graded nano-materials will be provided for inclusion in future publications on advanced structures.

Keywords:

• Energy absorption
• advanced FGM
• artificial intelligence
• composite structure
• viscoelastic parameters

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Jianbing Lingyan Project (International Cooperation Project) of Zhejiang Province (2023c04050); Zhejiang Province Basic Public Welfare Research Program Project (LGG21F040002); Science and Education Integration Project of Zhejiang Institute of Mechanical & Electrical Engineering (A-0271-23-202); Open Fund of Hubei Provincial Key Laboratory of Electronic Manufacturing and Packaging Integration (EMPI2023007). The authors present their appreciation to King Saud University for funding this research through Researchers Supporting Project number (RSPD2023R533), King Saud University, Riyadh, Saudi Arabia.