Quality-centric digital twins in additive manufacturing: a review of the state-of-the-art, challenges, and future directions

Additive Manufacturing (AM) transformed the production landscape, yet quality-related challenges continue to hinder its full potential. To address these issues, various approaches have been explored, including Design of Experiments (DOE), numerical simulations, data-driven methods, and the emerging technology of Digital Twin (DT) – a virtual replica of a physical entity enabling bi-directional data exchange and real-time interaction. It stands out by offering integrated monitoring, control, and predictive capabilities. Despite recent advancements, DTs in AM remain underdeveloped, especially in addressing quality concerns. This systematic review comprehensively focuses on DTs specifically designed to enhance AM part quality – an area overlooked in previous reviews. We examine critical DT development stages, including design, implementation, data processing and intelligence, and verification and validation. Our analysis reveals two key findings. First, only 16 studies from the literature meet the strict criteria for quality-centric, bi-directional DTs, highlighting the field’s immaturity and fragmentation. Second, most implementations are process-specific, with limited multitasking capabilities and over-reliance on sensor data, inadequately addressing critical quality attributes. Based on these findings, we propose six future research directions, namely establishing a comprehensive quality-focused DT framework, integrating multiscale modelling, enabling smarter DTs, creating a unified platform for diverse AM processes, improving data synchronisation, and enhancing data security.