Intelligent composite 3D printing: the role of artificial intelligence, machine learning, and in-situ monitoring in next-generation additive manufacturing

This narrative review synthesizes recent advances at the intersection of artificial intelligence (AI), machine learning (ML), and composite additive manufacturing (AM) by qualitatively analyzing peer-reviewed journal articles, authoritative review papers, and selected conference literature across materials science, manufacturing, and data-driven engineering. A narrative approach is appropriate for this rapidly evolving, interdisciplinary domain, where heterogeneous platforms, data modalities, and modeling strategies limit strict systematic comparison; therefore, the literature is thematically organized along the composite AM lifecycle to highlight trends, capabilities, and research gaps. Composite 3D printing, embedding fibers or functional fillers within printed matrices, enables lightweight, customizable, high-performance components but remains constrained by anisotropic properties, process instability, and inconsistent quality. The review examines how AI/ML supports (i) feedstock and composite material design, including data-informed formulation screening and property prediction; (ii) in-situ monitoring using vision, thermal, acoustic, and other sensing streams coupled with learning-based defect detection; (iii) adaptive and closed-loop process control, including reinforcement-learning and hybrid controller architectures; and (iv) digital twin frameworks augmented by data analytics and physics-informed models for predictive quality assurance and part performance forecasting. Application-oriented case studies in aerospace, biomedical engineering, automotive/consumer products, and construction are discussed to demonstrate practical impact and industrial relevance. Finally, key limitations, data scarcity and labeling burden, model generalizability across machines/materials, interpretability and trust, and system integration and standardization, are critically assessed, and future directions toward autonomous, sustainable, and secure intelligent composite manufacturing are outlined.