Artificial intelligence-enabled material design for automated manufacturing of tailored composites – a review

Advanced manufacturing techniques like Automated Fiber Placement (AFP), through their capability for precise fabrication of intricate design features, have created new opportunities for the development of novel composite architectures tailored to specific functional requirements. The integration of Artificial Intelligence (AI) further amplifies this potential by facilitating rapid exploration of this expansive untapped design space, thereby expanding the range of viable material configurations available to composite engineers. This review examines the key technological enablers - architected design strategies, advanced manufacturing practices, and AI-driven material discovery, that are essential for establishing an automated design framework for continuous fibre composites. Recent advancements in these domains are critically examined, highlighting the roadblocks and challenges impeding their integration into a unified approach. Two conceptual frameworks are proposed to demonstrate the feasibility of this approach, detailing implementation roadmaps and supporting data strategies. By bringing these ideas together, this review seeks to establish new directions for composite material innovation, accelerate the product development cycle, and support the emergence of next-generation automated composite design paradigms. • AI-driven material discovery for composites: capabilities, challenges, and future • Robust multi-scale AFP process modelling framework: implementation strategies • Generalised representation and optimisation method for meso-architected composites • Prospective inverse design frameworks for tailored continuous fibre composites