A review of machine learning applications in polymer composites: advancements, challenges, and future prospects

Abstract

Machine learning (ML) is revolutionizing the development and optimization of polymer composites by enabling data-driven insights into material design, manufacturing processes, and property prediction. Polymer composites, widely used in aerospace, automotive, biomedical, and construction industries, require precise engineering to achieve desired mechanical, thermal, and physical properties. Traditional methods for predicting composite behavior and optimizing production are often time-consuming and resource-intensive. ML techniques such as supervised, unsupervised, and deep learning offer an efficient alternative by analyzing large datasets, identifying patterns, and making accurate predictions without the need for extensive physical testing. This review examines the integration of ML into polymer composite research, highlighting its role in material discovery, performance prediction, and manufacturing process optimization. Case studies illustrate how ML algorithms have successfully enhanced property estimation, reduced defects, and accelerated the identification of novel composite formulations. However, challenges such as limited standardized datasets, model interpretability, and the need for domain-specific knowledge hinder broader adoption. Addressing these issues is crucial for advancing AI-driven composite development. Despite its potential, the adoption of ML in polymer composite manufacturing remains limited. Many industries still rely on conventional trial-and-error methods, leading to inefficiencies in material selection, process control, and quality assurance. This review underscores the importance of integrating AI-driven solutions to improve cost-effectiveness, reduce human errors, and streamline production workflows. By overcoming current challenges, ML can facilitate the development of next-generation high-performance polymer composites with superior mechanical strength, durability, and environmental sustainability.