Industrial digital twin for intelligent manufacturing: Key technologies, core challenges, applications, and forward-looking trends

Industrial digital twin (IDT) technology builds high-fidelity virtual representations of physical systems to support real-time synchronization, predictive analytics, and intelligent decision-making across the lifecycle of industrial assets. As a core enabler of Industry 5.0 and intelligent manufacturing initiatives, IDT enhances operational efficiency, reduces resource consumption, and strengthens system resilience by integrating sensing, modeling, simulation, and control within a unified cyber–physical framework. This review summarizes key advances in IDT research. It outlines enabling technologies with a focus on multi-scale modeling, real-time data fusion, high-performance simulation, artificial intelligence (AI) and machine learning analytics, and extended-reality (XR)-assisted visualization. Representative applications are discussed in areas such as intelligent manufacturing, equipment health management, supply-chain optimization, and energy-efficient operations, supported by industrial case evidence. Current challenges include fidelity assurance, heterogeneous data management, system integration complexity, organizational adaptation, and quantitative evaluation of investment value. Existing solution strategies are synthesized from both technological and management perspectives. Emerging trends are highlighted, including the integration of IDT with AI foundation models, quantum and edge computing, next-generation communication networks, and blockchain-based trust infrastructures. These developments are expected to broaden application domains and shape future industrial ecosystems.