Artificial Intelligence-Driven Design and Additive Manufacturing of Polymer Composites for Next-Generation Defence Applications: A Comprehensive Review

What happens when three of the most disruptive technologies of our era converge on a single application domain? This review answers that question for polymer composites in defence. The simultaneous maturation of machine learning (ML), additive manufacturing (AM), and advanced polymer composite science is creating design and fabrication capabilities that none of these technologies could deliver independently. We present what we believe to be the first analytically unified treatment of all five interconnected domains—ML, polymer matrix composites, AM processes, AI-driven mechanical design, and defence-specific structural applications—synthesised through a single, coherent defence-technology lens. Our analysis reveals that physics-informed machine learning (PIML) is uniquely positioned to address the classified-data bottleneck that renders conventional deep learning impractical for military composite design; that multi-modal in-situ AI monitoring is already achieving defect detection accuracies exceeding 94% during fabrication; that AI-driven topology optimisation is demonstrating mass reductions of 15–28% in unmanned aerial vehicle (UAV) structural components and specific energy absorption improvements of 22% in blast-attenuating armour cores relative to engineer-designed equivalents; and that digital twin frameworks are capable of compressing current two-to-four-year military qualification cycles into simulation-executable timelines measured in days. We systematically identify three high-priority research gaps—multi-threat Pareto optimisation frameworks, explainable AI qualification pathways, and federated learning architectures for classified data—and propose a structured ten-year technology roadmap toward autonomous, battlefield-deployable, AI-certified polymer composite manufacturing.