Cerebrum twin: A 6D semantic digital twin of multi-lobe digital brain functions for human-centric Industry 5.0

Abstract

Industry 5.0 highlights the need for human-centric and adaptive intelligence in smart manufacturing. This paper proposes the Cerebrum Twin (CT), a brain-inspired, six-dimensional semantic digital twin (SDT) system that unifies five human-like senses, including listening, speaking, reading, writing, and looking, within a cohesive multi-lobe digital brain framework. CT integrates real-time physical signals from force, vibration, and vision sensors by leveraging a synergistic ensemble of advanced artificial intelligence (AI) modules, such as Extreme Gradient Boosting (XGBoost), ConvNeXt V2, Efficient Sub-Pixel Convolutional Networks (ESPCN), stacked sparse autoencoder with supervision (SSAES), large language models (LLM), and reinforcement learning (RL). Uniquely, CT establishes a closed-loop semantic feedback mechanism, enabling dynamic perception, multimodal semantic abstraction, signal-driven prediction, adaptive parameter optimization, and intuitive voice-based human interaction. This holistic integration bridges the physical, semantic, and cognitive layers of CNC machining, supporting robust, transparent, and operator-oriented decision-making. The proposed system was validated through ultrasonic vibration-assisted blade dicing (UVABD) experiments. CT reduced dicing force prediction error by 39.86 %, improved tool wear prediction accuracy by 29.59 %, and decreased edge chipping severity by 60.47 % compared to the baseline model. These results demonstrate that a semantically empowered, multisensory digital twin (DT), enabled by real-time physical–semantic–AI fusion and human-in-the-loop optimization, can significantly enhance intelligent manufacturing performance and fulfill the vision of Industry 5.0.