Hybrid digital twins for smart manufacturing: Architectures, fusion paradigm, and implementation challenges

Abstract

As a high-fidelity representation of physical objects, the digital twin (DT) emerges as a crucial enabling tool supporting intelligent monitoring, prediction, and decision-making for smart manufacturing. To achieve reliable, accurate, and explainable DT modeling under dynamic conditions, it is necessary to integrate multiple models, including first-principles knowledge, data-driven algorithms, and simulation. Furthermore, with the emergence of state-of-the-art artificial intelligence (AI) technologies, such as Generative AI and Large Language Models, new drivers for DT modeling can be provided. However, the specific paradigm for hybridizing these models varies significantly depending on the application scenario, the object, and the critical requirements. This diversity poses a significant challenge for systematically selecting and combining modeling techniques in smart manufacturing. This review addresses this gap by providing a systematic exploration of the Hybrid Digital Twin (HDT) modeling paradigm, which focuses on the integration of multiple heterogeneous models. Therefore, this paper aims to: (1) clarify the architecture and core characteristics of HDT; (2) categorize critical technologies and fusion paradigms for HDT implementation; and (3) outline potential future research directions. It is hoped that this paper will serve as a systematic reference for researchers and engineers seeking to apply HDT to build more accurate, reliable, and adaptive DT applications.