Artificial Intelligence in Polymer Composites: A Review of Machine Learning Applications

Author(s): Emekwisia, Chukwudubem C, Oyesetan, Kolade O, Onuoha, Precious C, Eso, John T, Nwakama, Precious O, Eshikhogie, Eshiozemogie L

Affiliation: Department of Metallurgical and Materials Engineering, Nnamdi Azikiwe University, Awka, Nigeria. Division of Electronic Engineering, Jeonbuk National University, South Korea. Council for the Regulation of Engineering in Nigeria (COREN), Abuja, Nigeria. Department of Civil Construction and Environmental Engineering, North Carolina State University, Raleigh, USA. Department of Research and Development, Obimo (Suzhou) Technology Co. Ltd, Kunshan, China. Department of Natural Resources and Environmental Design, North Carolina Agricultural and Technical State University, Greensboro, USA. *Corresponding Email: cc.emekwisia@unizik.edu.ng

Page No: 1-18

Volume, Issue & Publishing Year: Volume 3 Issue 11,Nov-2026

Journal: International Journal of Advanced Engineering Application (IJAEA)

ISSN NO: 3048-6807

Abstract:
Polymer matrix composites (PMCs) have become important structural and functional materials because their matrix chemistry, reinforcement type, architecture, interface, and processing history can be tailored to meet application-specific requirements. Nevertheless, the development of reliable composite systems remains difficult because material performance emerges from coupled phenomena spanning molecular, microstructural, laminate, component, and environmental scales. Conventional experimental campaigns are consequently expensive and time-consuming, while high-fidelity numerical simulations can become computationally demanding when many formulation and process variables must be explored simultaneously. Machine learning (ML) and broader artificial intelligence (AI) methods provide a complementary approach by learning relationships from experimental, computational, imaging, and process-monitoring data and using those relationships for prediction, optimization, classification, and inverse design. This review synthesizes recent developments in AI-assisted polymer-composite research, with emphasis on literature published from 2018–2024. The review covers material design and selection, mechanical and functional-property prediction, multiscale modelling, manufacturing-process optimization, additive manufacturing, defect detection, damage prognosis, fire performance, tribology, explainable AI, physics-informed learning, and digital-twin-oriented workflows. The literature indicates that ML is most mature for tabular prediction problems and increasingly capable in image-based inspection and surrogate modelling of expensive simulations. However, apparently high prediction accuracy does not necessarily establish generalizability: many studies are limited by small datasets, correlated observations, inconsistent testing protocols, leakage between training and test sets, narrow material families, and insufficient external validation. Accordingly, AI should be viewed as a decision-support and acceleration layer rather than a replacement for materials science, mechanics, or experimental validation.

Keywords: Polymer Matrix Composites, Machine learning, Artificial Intelligence, Property Prediction, Process Optimization.

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