Artificial intelligence (AI) is revolutionizing composites research by enabling data-driven design, accelerated materials discovery, and predictive modeling of complex behaviors. Machine learning algorithms can analyze vast datasets from experiments and simulations to identify patterns and optimize process-structure-property relationships in fiber-reinforced polymer composites.
AI techniques are increasingly used for defect detection in manufacturing, real-time process monitoring, and inverse design of composite architectures with tailored properties.
Integrating AI with multiscale modeling frameworks can significantly enhance the predictive capabilities for mechanical performance, failure mechanisms, and long-term durability of composite materials under extreme conditions. This synergy enables accelerated design iterations, improved reliability, and optimization of processing parameters.