While additive manufacturing has demonstrated tremendous potential across diverse sectors, significant challenges remain in achieving the reliability, consistency, qualification, and scalability required for widespread industrial deployment. Addressing these challenges demands closer integration of materials science, process physics, and intelligent manufacturing technologies capable of enabling more predictable, autonomous, and industrially robust production systems.

This special issue aims to advance the scientific understanding and intelligent control of additive manufacturing by bringing together advances in materials, manufacturing processes, and emerging intelligent technologies. We invite original research, reviews, and perspectives on artificial intelligence and machine learning, physics-informed modelling, digital twins, and data-driven approaches to additive manufacturing. Particular emphasis is placed on material and process–structure–property relationships, in-situ monitoring, defect detection, process optimisation, quality prediction, and adaptive closed-loop control. Contributions addressing uncertainty quantification, model interpretability, data quality, transferability, and reproducibility are particularly encouraged. Studies demonstrating how intelligent systems can support material development, process qualification, autonomous production, and industrially relevant validation are also welcomed. Overall, this special issue seeks to connect fundamental materials research with reliable digital methodologies, enabling more predictable, efficient, sustainable, and industrially deployable additive manufacturing processes.