Lingjun Mao
Papers
2
Total Citations
62
H-Index
2
About
Dr. Lingjun Mao is a leading researcher at the intersection of digital twin technology, intelligent manufacturing, and robotic machining. Their work focuses on bridging the gap between virtual models and physical production systems, particularly for large-scale industrial components. Dr. Mao’s most cited paper, “Knowledge graph and function block based Digital Twin modeling for robotic machining of large-scale components” (2023, 47 citations), introduces a novel framework that integrates semantic knowledge graphs with modular function blocks to enable real-time, adaptive control of robotic machining processes—a significant step toward fully autonomous manufacturing. Building on this, their recent work, “Multi-domain data-driven chatter detection in robotic milling under varied robot poses based on directional attention mechanism” (2025, 15 citations), advances machining stability by employing attention-based deep learning to detect chatter across diverse robotic configurations. This work addresses a critical challenge in precision manufacturing, where tool vibration can compromise quality. Dr. Mao’s contributions have been widely recognized for their practical impact on Industry 4.0, earning citations from both academic and industrial researchers. Their research not only pushes the boundaries of digital twin and AI-driven manufacturing but also provides scalable solutions for real-world factory automation.
Research Focus
Key Achievements
Top Papers
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