Seven-Layer Architecture and Key Technology Integration in the Industrial Metaverse: Transforming Advanced Manufacturing Through Virtual-Physical Convergence
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Abstract
The Industrial Metaverse (IMV) represents a transformative paradigm for advanced manufacturing, integrating immersive virtual environments with physical production systems through a seamless digital-physical continuum. Unlike consumer metaverse applications focused on social interaction and entertainment, the IMV is purposefully engineered to enhance manufacturing efficiency, innovation velocity, and workforce collaboration through the convergence of seven enabling technology clusters: Internet of Things (IoT), Digital Twins (DT), Artificial Intelligence (AI), Virtual/Augmented Reality (VR/AR), blockchain, 5G/6G communications, and edge-cloud computing. This paper proposes a systematic seven-layer IMV architecture—spanning perception, network, data, platform, application, security, and management layers—and provides a comprehensive analysis of the enabling technologies at each layer and their inter-layer interaction mechanisms. A quantitative technology interaction strength analysis reveals that Digital Twins exhibit the highest average coupling index (0.847) with other IMV technologies, confirming their role as the central integration fabric of the IMV ecosystem. Case study analysis across seven manufacturing sectors demonstrates IMV adoption rates ranging from 6% (food and beverage, 2020) to 52% (aerospace, 2022), with projected sector-wide adoption exceeding 60% by 2025 for technology-intensive industries. Worker training and product design emerge as the lifecycle stages with highest IMV impact scores (85% and 82% respectively), reflecting the established maturity of VR-based training applications and AI-assisted generative design tools. Key unresolved challenges identified include the personalization-implementation gap, cross-domain technology interoperability, and data sovereignty in shared IMV environments. A structured research agenda addressing these challenges is proposed to guide the IMV research community
