New Materials, Compounds and Applications

Polymeric composites reinforced with particles and fibers are fundamental to modern engineering, with diverse applications in aerospace, automotive, marine, and structural sectors. The challenge of accurately predicting mechanical properties in these heterogeneous materials remains pivotal for optimizing design, ensuring performance reliability, and achieving cost-efficiency. This review critically examines state-of-the-art modeling approaches, including analytical, empirical, numerical, machine learning, and hybrid methods, synthesizing insights from existing literature. Comparative analyses using case studies of carbon fiber-reinforced polymers (CFRP), glass fiber-reinforced polymers (GFRP), and particulate-reinforced composites evaluate each method’s accuracy, computational efficiency, and practical applications. Results indicate that while analytical models such as the Rule of Mixtures offer rapid estimates with 10-30% error, advanced numerical methods like finite element analysis achieve 5-10% accuracy but demand greater computational resources. Machine learning models outperform traditional methods, achieving >95% accuracy when adequate training data is available. This review offers engineers and researchers a systematic framework for selecting predictive models tailored to specific applications, emphasizing the trade-offs between computational cost and accuracy. Future work should prioritize integrating hybrid methods and enhancing machine learning to address data availability and scalability challenges.