Digital Twin‐Enabled Additive Manufacturing: A Comprehensive Review of Architectures, Integration Layers and Operational Maturity

ABSTRACT

Digital twin‐enabled additive manufacturing (DT‐AM) represents a critical advancement in industrial intelligence, yet its current fragmented implementations necessitate a comprehensive systematic analysis. This review rigorously examines DT‐AM architectures, integration frameworks, and pathways towards achieving autonomous and scalable production environments. Employing a structured multidimensional taxonomy, DT‐AM systems are categorised based on functional scope (component, asset, system, process twins), integration depth (digital model, digital shadow, digital twin) and operational sophistication (ranging from descriptive to fully autonomous twins). The synthesis highlights a pronounced emphasis in existing literature on simulation and control functionalities, with notable gaps identified in validation frameworks and intelligence‐driven decision‐making mechanisms. Major challenges, including data heterogeneity, computational scalability and inadequate validation strategies, currently hinder seamless interoperability and broader industrial adoption. The analysis further identifies promising technological solutions such as agentic artificial intelligence, secure digital thread infrastructures, hybrid cloud‐edge computing and human‐centric augmented and virtual reality interfaces. Crucially, the paper underscores the imperative of software standardisation as foundational to the progression of DT‐AM systems. Five strategic research trajectories are proposed to systematically bridge existing technological and operational gaps, fostering resilient, interoperable and human‐centric cyber‐physical manufacturing ecosystems. Ultimately, this comprehensive review establishes a structured foundation for advancing DT‐AM, guiding future scholarly and industrial efforts towards cohesive, intelligent and scalable production systems.