A Framework for Integrating Vision Transformers with Digital Twins in Industry 5.0 Context

The transition from Industry 4.0 to Industry 5.0 gives more prominence to human-centered and sustainable manufacturing practices. This paper proposes a conceptual design framework based on Vision Transformers (ViTs) and digital twins, to meet the demands of Industry 5.0. ViTs, known for their advanced visual data analysis capabilities, complement the simulation and optimization capabilities of digital twins, which in turn can enhance predictive maintenance, quality control, and human–machine symbiosis. The applied framework is capable of analyzing multidimensional data, integrating operational and visual streams for real-time tracking and application in decision making. Its main characteristics are anomaly detection, predictive analytics, and adaptive optimization, which are in line with the objectives of Industry 5.0 for sustainability, resilience, and personalization. Use cases, including predictive maintenance and quality control, demonstrate higher efficiency, waste reduction, and reliable operator interaction. In this work, the emergent role of ViTs and digital twins in the development of intelligent, dynamic, and human-centric industrial ecosystems is discussed.