Artificial Intelligence-Assisted Prediction of Mechanical Performance of Nano-Modified Polymer Composites for Lightweight Structures

Abstract

Nano-modified polymer composites have been of particular interest for lightweight structural applications because nanoscale reinforcements can increase stiffness, strength, fracture resistance, fatigue performance and damage tolerance without a large increase in density. However the mechanical response of these materials is influenced by several interacting parameters (polymer matrix characteristics, reinforcement type, nanoparticle concentration, particle size, surface functionalization, fiber architecture, dispersion quality, curing conditions and manufacturing process) which result in nonlinear relationships that conventional trial-and-error methods are not well suited to optimize composite formulations.
This study proposes an artificial intelligence-assisted framework for predicting the mechanical performance of nano-modified polymer composites using machine-learning techniques. A structured materials dataset is proposed by combining experimentally reported composite formulations and processing conditions from peer-reviewed literature. The principal input variables include matrix type, reinforcement type, nanoparticle type, nanoparticle size, nanoparticle loading, fiber volume fraction, fiber orientation, curing temperature, curing duration, processing method, and selected microstructural descriptors. The principal output variables are tensile strength, tensile modulus, flexural strength, flexural modulus, interlaminar shear strength, and impact strength. Multiple regression and ensemble learning techniques, including multiple linear regression, support vector regression, random forest, gradient boosting, extreme gradient boosting, and artificial neural networks, are considered for comparative prediction.
The framework further incorporates explainable artificial intelligence using SHapley Additive exPlanations to identify the relative contribution of material and processing parameters. Cross-validation, independent testing, error analysis, and sensitivity analysis are included to minimize overfitting and improve model reliability. The proposed approach is intended to establish a transparent relationship between composition, processing, microstructure, and mechanical performance. Recent research demonstrates that such explainable approaches can successfully predict nanomodified composite properties while identifying influential processing and material parameters.
The proposed framework provides a pathway toward rapid screening and AI-assisted design of lightweight polymer composite materials. Rather than replacing experimental characterization, the model is intended to reduce unnecessary experimental combinations and identify promising material formulations for subsequent laboratory validation.